G

Georgina Weston

Kitman Labs Position Profile

Back End Engineer Position Profile at Kitman Labs

This profile documents the responsibilities, decision frameworks, systems, and practices associated with my Back End Engineer position within a high-integrity sports technology environment. It provides structured context around platform complexity, data criticality, operational standards, and delivery expectations across production systems serving elite sports organisations.

The content is factual, stable, and intended for consistent reference across technical, architectural, and capability-led discussions rather than as a chronological narrative of activity.

Back End Engineer Position Overview

Position
Back End Engineer

Organisation
Kitman Labs Ltd.

Dates
November 2022 to July 2024

Position Type
Professional, Remote

Domain
Sports technology, data platforms, performance analytics, distributed systems

Back End Engineer Position Summary

As a Back End Engineer, I was responsible for backend systems supporting data ingestion, transformation, validation, and delivery for elite sports organisations including national federations and professional leagues. My coverage included data migrations, backend services, third-party integrations, staging environments, deployment pipelines, monitoring, security controls, and technical documentation.

I operated within production environments governed by strict requirements for data integrity, auditability, reliability, and multi-region availability, including coexistence with a legacy C# and .NET platform hosted across Azure-based services.

Platform and Competition Context

My work supported systems used across the Football Association, Irish Rugby Football Union, Major League Soccer, MLS NEXT, National Football League, National Women’s Soccer League, Professional Game Match Officials Limited, Premier League, and Rugby Football Union. The operational scope covered athlete monitoring systems, performance analysis platforms, and officiating or operational decision-support systems across domestic and international competition environments.

Data Migrations and Data Integrity

Area Summary

  • This area focused on the safe movement of data between legacy and active systems.
  • It covered migration design, sequencing, execution, validation, and auditability.
  • It sat close to production risk because downstream systems depended on accurate data.
  • It required careful control of schema expectations, dependency order, and release timing.
  • It involved balancing speed of delivery with depth of validation and rollback readiness.
  • It relied on repeatable methods rather than one-off scripts wherever possible.
  • It supported staging-to-production transitions with strict integrity checks.
  • It prioritised data trustworthiness over convenience or acceleration.

Responsibilities

  • I designed migration approaches for legacy and active platform transitions.
  • I prepared data movement logic for structured and repeatable execution.
  • I validated source and target schema compatibility before migration.
  • I defined sequencing to protect dependent datasets and downstream consumers.
  • I automated verification checks using code and database queries.
  • I reviewed anomalies and resolved issues through targeted corrective changes.
  • I supported staging and production migration readiness with rollback awareness.
  • I documented migration behaviour, assumptions, and outcomes for reference.

Decision Criteria

  • Accuracy of data transferred between legacy and active systems
  • Compatibility between source and target schemas
  • Programmatic validation capability
  • Repeatability of migration logic
  • Traceability of records for audit
  • Risk exposure during staging-to-production transitions
  • Rollback and recovery capability
  • Protection of downstream data consumers

Constraints

  • Historical inconsistencies in legacy datasets
  • Fixed production dependencies
  • Limited migration windows
  • Mandatory rollback safety
  • Existing AWS infrastructure patterns
  • Predefined schema expectations
  • Platform-level data integrity rules
  • Dependency ordering between datasets

Trade-Offs

  • Migration speed versus validation depth
  • Generic tooling versus dataset-specific scripts
  • Automated checks versus manual inspection
  • Minimal schema change versus corrective restructuring
  • Immediate completion versus repeatability
  • Parallel execution versus controlled sequencing
  • Validation strictness versus anomaly tolerance
  • Migration scope versus operational risk

Prioritisation Thresholds

  • Data accuracy over delivery speed
  • Validation coverage over throughput
  • Automation for repeatable migrations
  • Manual intervention only when automation was unsafe
  • Rollback readiness before release
  • Staging parity before approval
  • Downstream dependency protection
  • Auditability before optimisation

Delivery Alignment

  • I designed ETL pipelines for schema enforcement.
  • I automated validation using RSpec and SQL.
  • I created reusable migration tooling.
  • I defined explicit sequencing.
  • I aligned staging environments with production expectations.
  • I conducted post-migration audits.
  • I resolved anomalies through targeted fixes.
  • I documented migration logic.

Tools and Systems

  • Ruby supported migration scripting and execution logic.
  • SQL supported validation, reconciliation, and audit queries.
  • RSpec supported automated checks around migration behaviour.
  • ETL pipelines supported controlled transformation and loading.
  • AWS-hosted environments shaped migration execution patterns.
  • Staging environments supported rehearsal and parity validation.
  • Relational schemas defined mapping and compatibility boundaries.
  • Audit records supported traceability and release confidence.

Backend Services and Data Processing

Area Summary

  • This area focused on backend services responsible for processing, storage, and delivery.
  • It covered service behaviour, background execution, transactional safety, and maintainability.
  • It operated within an existing Ruby on Rails architecture with defined service boundaries.
  • It had to coexist with legacy C# and .NET services hosted in Azure environments.
  • It required predictable processing under load rather than fragile high-throughput shortcuts.
  • It depended on clear contracts between services, models, and execution paths.
  • It prioritised stable behaviour and clarity over unnecessary architectural churn.
  • It supported evolving platform requirements without destabilising production systems.

Responsibilities

  • I implemented backend services in line with established platform conventions.
  • I structured data models to support extension and predictable system behaviour.
  • I configured background tasks with explicit retry and logging behaviour.
  • I preserved transactional integrity across service and database operations.
  • I handled error states explicitly to reduce ambiguity in production.
  • I supported coexistence between modern services and legacy platform components.
  • I maintained service boundaries to reduce coupling and preserve clarity.
  • I documented execution paths and service behaviour for shared understanding.

Decision Criteria

  • Long-term maintainability
  • Predictability of background processing
  • Clarity of service boundaries
  • Compatibility with platform architecture
  • Data consistency across services
  • Error handling transparency
  • Support for evolving requirements
  • Observability of execution behaviour

Constraints

  • Existing Ruby on Rails architecture
  • Established service boundaries
  • Legacy C# and .NET system coexistence hosted on Azure App Services
  • Azure-hosted APIs and Azure SQL databases forming part of the legacy platform
  • Performance requirements under load
  • Limited tolerance for runtime failure
  • Existing relational database schemas
  • Shared infrastructure dependencies across AWS and Azure environments

Trade-Offs

  • Flexible schemas versus strict contracts
  • Immediate delivery versus structural consistency
  • Throughput versus observability
  • Consolidation versus coexistence
  • Simplicity versus extensibility
  • Background concurrency versus predictability
  • Validation strictness versus ingestion speed
  • Refactoring versus stability

Prioritisation Thresholds

  • Maintainability over short-term acceleration
  • Clear data contracts for cross-service interaction
  • Defined retry behaviour before deployment
  • Predictable execution over maximum throughput
  • Legacy support when replacement risk was high
  • Validation before optimisation
  • Stability before architectural change
  • Observability before scaling

Delivery Alignment

  • I implemented services following platform conventions.
  • I structured data models for extension.
  • I configured background tasks with retries and logging.
  • I preserved transactional integrity.
  • I handled error states explicitly.
  • I supported legacy systems alongside modern services.
  • I preserved service boundaries.
  • I documented execution paths.

Tools and Systems

  • Ruby on Rails provided the primary backend application framework.
  • ActiveRecord supported relational modelling and data access patterns.
  • Background workers supported scheduled and asynchronous processing.
  • AWS-hosted services supported modern backend runtime environments.
  • Azure App Services supported legacy platform coexistence.
  • Azure SQL supported legacy data storage and access patterns.
  • C# and .NET services shaped interoperability requirements.
  • Logging and observability tooling supported behavioural visibility.

Third-Party Integrations and External Systems

Area Summary

  • This area focused on the safe and reliable exchange of data with external systems.
  • It covered ingestion design, schema mapping, scheduling, retries, and monitoring.
  • It operated under external variability, including rate limits, uptime changes, and data quality issues.
  • It required internal consistency while accommodating provider-specific differences.
  • It balanced freshness of data against stability of ingestion flows.
  • It relied on reusable middleware where repeated integration patterns existed.
  • It prioritised validation before persistence to reduce contamination of internal systems.
  • It supported scale and reliability through controlled, observable processing paths.

Responsibilities

  • I analysed requirements across internal and external APIs.
  • I implemented middleware using shared gems and service libraries.
  • I mapped external provider schemas to internal models.
  • I configured scheduled requests with awareness of rate limits and stability.
  • I implemented retries, backoff strategies, and structured error handling.
  • I enabled monitoring to surface integration failures and degraded behaviour.
  • I optimised processing flows for reliability and scale.
  • I documented integration logic for internal reference and continuity.

Decision Criteria

  • Security of data exchange
  • Scalability of ingestion pipelines
  • Reliability of scheduled ingestion
  • Accuracy of schema mapping
  • Speed of error detection
  • Maintainability of integration logic
  • Compliance with rate limits
  • Diagnostic visibility

Constraints

  • External API rate limits
  • Variable data quality
  • Dependency on external uptime
  • Fixed external schemas
  • Platform business rules
  • Existing middleware patterns
  • Scheduling limitations
  • Monitoring tool availability

Trade-Offs

  • Real-time ingestion versus scheduled ingestion
  • Strict validation versus tolerance for incomplete data
  • Custom logic versus shared middleware
  • Ingestion frequency versus stability
  • Transformation complexity versus simplicity
  • Internal consistency versus external variance
  • Retry aggressiveness versus rate-limit safety
  • Observability depth versus overhead

Prioritisation Thresholds

  • Data correctness over ingestion speed
  • Scheduled ingestion when reliability varied
  • Middleware reuse for repeated patterns
  • Rate-limit compliance over freshness
  • Validation before persistence
  • Monitoring before optimisation
  • Resilience before performance tuning
  • Documentation before expansion

Delivery Alignment

  • I analysed integration requirements across internal and external APIs.
  • I implemented middleware using shared internal gems and service libraries.
  • I completed schema mapping between providers and internal models.
  • I configured scheduled requests with rate-limit awareness.
  • I implemented error handling with retries, backoff strategies, and structured logging.
  • I enabled monitoring using Sentry and Datadog.
  • I optimised processing paths for scale and reliability.
  • I documented integration logic.

Tools and Systems

  • External APIs provided inbound and outbound integration boundaries.
  • Internal Ruby gems supported reusable integration patterns.
  • Service libraries supported shared processing logic.
  • Scheduled jobs supported reliable timed ingestion flows.
  • Structured logging supported diagnosis of failure states.
  • Retry and backoff logic supported resilience under instability.
  • Sentry supported failure visibility and alerting context.
  • Datadog supported monitoring across integration pathways.

Staging Environments and Deployment Pipelines

Area Summary

  • This area focused on safe promotion of changes through controlled environments.
  • It covered staging fidelity, automated pipelines, release sequencing, and rollback readiness.
  • It operated across multi-region infrastructure where consistency mattered.
  • It balanced delivery speed with release confidence and operational safety.
  • It depended on predictable build and deployment behaviour across shared repositories.
  • It required visibility into failures rather than opaque pipeline automation.
  • It supported incremental delivery to reduce blast radius and improve recovery.
  • It prioritised repeatable release practices over rushed deployment activity.

Responsibilities

  • I aligned staging environments closely with production expectations.
  • I supported deployments across services running on EC2-backed infrastructure.
  • I integrated automated tests into CI/CD workflows.
  • I adjusted branch strategies to reduce risk during concurrent change.
  • I triaged build and deployment failures with engineers and QA.
  • I monitored release stages to improve visibility and recovery confidence.
  • I stabilised release cadence through smaller, incremental delivery patterns.
  • I validated rollback procedures before promotion into production.

Decision Criteria

  • Fidelity between staging and production
  • Early detection of configuration issues
  • Reduction of deployment risk
  • Support for incremental releases
  • Transparency of pipeline behaviour
  • Test coverage enforcement
  • Multi-region consistency
  • Recovery capability

Constraints

  • Existing CI/CD tooling
  • Multi-region infrastructure
  • Shared repositories
  • Branching conventions
  • Coordination across teams
  • Limited deployment windows
  • Production uptime requirements
  • Toolchain dependencies

Trade-Offs

  • Deployment speed versus stability
  • Pipeline complexity versus clarity
  • Release frequency versus confidence
  • Automation versus manual oversight
  • Parallel changes versus serial validation
  • Rapid rollback versus prevention
  • Pipeline strictness versus flexibility
  • Local optimisation versus global consistency

Prioritisation Thresholds

  • Stability over deployment speed
  • Automated testing before approval
  • Small releases over large batches
  • Root-cause identification before retry
  • Staging parity before promotion
  • Visibility before optimisation
  • Repeatability before acceleration
  • Safety before convenience

Delivery Alignment

  • I aligned staging environments closely with production across AWS regions.
  • I supported deployments for services running on EC2-backed infrastructure.
  • I integrated automated tests into pipelines using CircleCI.
  • I adjusted branch strategies to reduce risk during concurrent changes.
  • I triaged failures with engineers and QA.
  • I monitored deployment stages using Datadog.
  • I stabilised release cadence through incremental delivery.
  • I validated rollback procedures prior to promotion.

Tools and Systems

  • Staging environments supported release rehearsal and validation.
  • Production AWS regions defined deployment consistency requirements.
  • EC2-backed services shaped deployment execution paths.
  • CircleCI supported automated build and test stages.
  • Shared repositories shaped integration and promotion workflows.
  • Branching strategies supported controlled concurrent delivery.
  • Datadog supported release-stage monitoring and failure visibility.
  • Rollback procedures supported recovery planning before promotion.

Monitoring, Reliability, and Performance

Area Summary

  • This area focused on keeping systems observable, stable, and predictable in production.
  • It covered metrics, alerts, tracing, load review, and bottleneck analysis.
  • It operated in environments with high ingestion volumes and limited tolerance for failure.
  • It balanced monitoring depth against noise, cost, and operational overhead.
  • It required actionable signals rather than large volumes of low-value telemetry.
  • It supported capacity planning and scaling decisions through behaviour-based visibility.
  • It prioritised diagnosis before blind optimisation or expansion.
  • It maintained reliability as platform usage evolved across services and regions.

Responsibilities

  • I selected metrics based on operational relevance and behavioural usefulness.
  • I enabled distributed tracing and diagnostic visibility where needed.
  • I configured alerts with thresholds intended to be actionable.
  • I reviewed system performance under representative load.
  • I identified and addressed bottlenecks proactively.
  • I monitored resource usage across AWS-hosted services.
  • I adjusted monitoring as usage patterns changed.
  • I supported stability during scaling events and increased demand.

Decision Criteria

  • Early issue detection
  • Actionable alert thresholds
  • Performance bottleneck visibility
  • Metric-to-behaviour correlation
  • Minimal monitoring overhead
  • Scalability of instrumentation
  • Diagnostic clarity
  • Operational predictability

Constraints

  • Existing monitoring tools
  • Infrastructure performance limits
  • High ingestion volumes
  • Alert fatigue risk
  • Data retention limits
  • Metric granularity limits
  • Shared dashboards
  • Cost considerations

Trade-Offs

  • Instrumentation depth versus overhead
  • Alert sensitivity versus noise
  • Proactive monitoring versus reactive analysis
  • Performance tuning versus cost
  • Granularity versus clarity
  • Real-time metrics versus batch analysis
  • Centralised monitoring versus service-level focus
  • Coverage versus maintainability

Prioritisation Thresholds

  • Early detection when impact risk was high
  • Actionable alerts only
  • Performance tuning when volume increased
  • Monitoring expansion during system growth
  • Stability before optimisation
  • Diagnosis before scaling
  • Capacity planning before saturation
  • Reliability over cost efficiency

Delivery Alignment

  • I selected metrics for operational relevance.
  • I enabled distributed tracing for diagnostics.
  • I configured alerts with thresholds.
  • I reviewed performance under load.
  • I identified and addressed bottlenecks proactively.
  • I monitored resource usage across AWS-hosted services.
  • I maintained stability during scaling events.
  • I adjusted monitoring as platform usage evolved.

Tools and Systems

  • Datadog supported metrics, dashboards, and alert visibility.
  • Sentry supported failure and exception monitoring.
  • Distributed tracing supported cross-service diagnosis.
  • AWS resource metrics supported infrastructure visibility.
  • Shared dashboards supported operational communication.
  • Load review processes supported performance evaluation.
  • Alert thresholds supported actionable incident response.
  • Capacity signals supported scaling and stability planning.

Security and Access Controls

Area Summary

  • This area focused on protecting sensitive data and controlling system access.
  • It covered authentication, least-privilege enforcement, encryption, and auditability.
  • It operated across modern and legacy services with different technical constraints.
  • It balanced security hardening with usability and delivery timelines.
  • It required consistency across services rather than isolated security decisions.
  • It prioritised prevention and remediation ahead of convenience or speed.
  • It supported partner expectations and platform-level compliance requirements.
  • It maintained security posture as an ongoing operational concern, not a one-off task.

Responsibilities

  • I enforced access using least-privilege principles.
  • I supported authentication mechanisms across relevant services and flows.
  • I applied encryption to sensitive paths and protected data movement.
  • I reviewed vulnerabilities and supported remediation activity.
  • I considered partner and platform compliance expectations in implementation choices.
  • I documented security posture and control behaviour for shared reference.
  • I reviewed controls periodically to support consistency over time.
  • I balanced practical usability with reduced exposure to operational risk.

Decision Criteria

  • Protection of sensitive data
  • Least-privilege access
  • Compliance alignment
  • Usability balance
  • Threat surface minimisation
  • Authentication robustness
  • Auditability
  • Consistency across services

Constraints

  • Existing authentication frameworks
  • Platform security standards
  • Infrastructure-level controls
  • Partner compliance expectations
  • System usability requirements
  • Legacy compatibility
  • Tooling limitations
  • Deployment timelines

Trade-Offs

  • Ease of access versus restriction
  • Centralised controls versus service-level enforcement
  • Security hardening versus delivery timelines
  • Authentication complexity versus usability
  • Encryption overhead versus performance
  • Policy strictness versus flexibility
  • Coverage versus operational friction
  • Automation versus manual review

Prioritisation Thresholds

  • Data protection over convenience
  • Access control when exposure risk existed
  • Security updates over delivery
  • Increased authentication only when justified
  • Vulnerability remediation without delay
  • Consistency before optimisation
  • Auditability before expansion
  • Prevention over recovery

Delivery Alignment

  • I enforced access through least-privilege principles.
  • I applied encryption to sensitive paths.
  • I integrated authentication mechanisms.
  • I supported multi-factor authentication.
  • I reviewed vulnerabilities.
  • I applied remediation promptly.
  • I documented security posture.
  • I reviewed controls periodically.

Tools and Systems

  • Authentication frameworks supported protected access pathways.
  • Multi-factor authentication supported higher-trust access control.
  • Encryption controls supported protection of sensitive data paths.
  • Infrastructure-level controls shaped practical enforcement boundaries.
  • AWS IAM supported access management within cloud environments.
  • Azure service controls supported legacy platform security requirements.
  • Vulnerability review processes supported remediation planning.
  • Audit records supported traceability and governance confidence.

Technical Documentation

Area Summary

  • This area focused on creating usable technical references for shared systems.
  • It covered architecture, integrations, data models, validation logic, and onboarding material.
  • It supported mixed audiences with different levels of technical confidence.
  • It balanced clarity and accessibility with precision and technical usefulness.
  • It relied on documentation staying aligned with live system behaviour.
  • It reduced dependence on informal knowledge held by individuals.
  • It supported onboarding, maintenance, and cross-team coordination.
  • It prioritised accuracy and maintainability over document volume.

Responsibilities

  • I authored architecture diagrams to explain system structure.
  • I documented integration flows and service relationships.
  • I specified data models and relevant structural behaviour.
  • I recorded validation logic and reference rules.
  • I structured guides to support navigation and practical use.
  • I maintained documentation as living artefacts as systems changed.
  • I supported onboarding through shared technical references.
  • I enabled cross-team alignment through documented understanding.

Decision Criteria

  • Accessibility for mixed audiences
  • Consistency with standards
  • Support for onboarding
  • Longevity across changes
  • Structural clarity
  • Reference usability
  • Accuracy
  • Maintainability

Constraints

  • Existing documentation formats
  • System complexity
  • Synchronisation with live systems
  • Time availability
  • Tooling constraints
  • Cross-team dependencies
  • Versioning requirements
  • Review cycles

Trade-Offs

  • Depth versus time
  • Precision versus accessibility
  • Comprehensive coverage versus targeted references
  • Immediate updates versus scheduled reviews
  • Narrative explanation versus schematic clarity
  • Centralisation versus duplication
  • Detail versus readability
  • Speed versus accuracy

Prioritisation Thresholds

  • Documentation required for shared systems
  • Greater depth for high-risk systems
  • Shared references over personal knowledge
  • Updates following behavioural changes
  • Clarity over completeness
  • Accuracy over speed
  • Accessibility over formality
  • Maintenance over expansion

Delivery Alignment

  • I authored architecture diagrams.
  • I documented integration flows.
  • I specified data models.
  • I recorded validation logic.
  • I structured guides for navigation.
  • I maintained documentation as living artefacts.
  • I supported onboarding through shared references.
  • I enabled cross-team alignment.

Tools and Systems

  • Architecture diagrams supported system-level understanding.
  • Integration flow documentation supported cross-service visibility.
  • Data model references supported structural consistency.
  • Validation logic records supported repeatable understanding of rules.
  • Onboarding guides supported faster knowledge transfer.
  • Shared documentation spaces supported team accessibility.
  • Versioned references supported change awareness over time.
  • Living documentation practices supported long-term maintainability.

Systems, Tools, and Platforms

My core systems, tools, and platforms included Ruby, C#, SQL, and JavaScript; Ruby on Rails, ActiveRecord, RSpec, and Jest; React for integration awareness and API support; AWS services including EC2, S3, Step Functions, IAM, and CloudWatch; Azure services including App Services, Azure SQL, and Azure-hosted legacy APIs and .NET services; CircleCI, Datadog, and Sentry; PostgreSQL, MySQL, and Azure SQL; and Git-based workflows with scheduled background workers and cron-based processing where required.

Position Scope and Engineering Environment

This position sat within distributed, remote-first engineering environments supporting national and international sports organisations. The systems involved high-volume, high-integrity data pipelines, multi-region infrastructure across EU and US coverage, collaboration with engineers, QA, product teams, analysts, and stakeholders, and production environments with limited tolerance for failure. The platforms underpinned performance analysis, athlete monitoring, officiating support, and operational decision-making where accuracy, traceability, and uptime were critical.