Data Engineer
Weekly Rate: $8,500/week
Overview
The Data Engineer designs and implements scalable data pipelines that transform raw squat tracking data into actionable insights for athletes, coaches, and HYROX organization. This role ensures reliable data flow from edge devices through real-time processing to analytics platforms, managing the complete data lifecycle from ingestion through archival.
Key Responsibilities
Data Pipeline Architecture - Design scalable data pipelines that handle high-velocity streams from 80+ concurrent tracking stations during peak competition periods. Implement architectures that balance real-time processing requirements with batch analytics needs.
Real-time Stream Processing - Implement streaming data processing using technologies like Apache Flink or Spark Streaming to provide sub-second latency for live competition updates. Ensure data streams remain synchronized across multiple processing stages.
Data Warehousing - Design and manage athlete performance data warehouses that support complex analytical queries while maintaining individual privacy. Implement partitioning strategies that optimize for both real-time access and historical analysis.
ETL Process Development - Build robust Extract, Transform, Load processes that clean, validate, and enrich raw sensor data for downstream analytics. Implement data quality checks that identify and handle anomalies without disrupting live operations.
Data Quality Assurance - Ensure data quality and consistency across all pipeline stages through automated validation, monitoring, and alerting systems. Implement reconciliation processes that detect and correct data discrepancies.
Schema Design and Evolution - Design flexible data schemas that accommodate system evolution without breaking existing analytics. Implement schema versioning and migration strategies that ensure backward compatibility.
Performance Optimization - Optimize data pipeline performance to handle peak loads during major championships with 10,000+ athletes. Implement caching strategies and query optimization that maintain sub-second response times.
Data Integration - Integrate squat tracking data with existing HYROX timing systems, leaderboards, and athlete management platforms. Design APIs and data contracts that ensure reliable inter-system communication.
Privacy and Compliance - Implement data anonymization and retention policies that comply with GDPR and other privacy regulations. Design systems that provide data value while protecting individual athlete information.
Monitoring and Observability - Build comprehensive monitoring for data pipeline health, including throughput metrics, latency tracking, and data quality indicators. Implement alerting that enables proactive issue resolution.
Required Skills
Data Engineering Excellence demonstrates extensive experience building production data pipelines that handle high-volume, high-velocity data streams reliably. They have designed systems that scale from hundreds to millions of events while maintaining data quality and low latency. Their experience includes managing the complete data lifecycle in production environments.
Stream Processing Expertise shows deep knowledge of real-time data processing patterns and technologies for handling continuous data streams efficiently. They understand the trade-offs between different streaming architectures and can design systems that balance latency, throughput, and reliability. Their implementations handle out-of-order data, late arrivals, and system failures gracefully.
Data Modeling and Architecture possesses strong foundation in data modeling principles for both transactional and analytical workloads with understanding of warehouse design patterns. They can design schemas that support efficient querying while maintaining data integrity and handling evolving requirements. Their architectures balance normalization with query performance needs.
Cloud and Distributed Systems exhibits practical experience with cloud data platforms and distributed processing frameworks that enable horizontal scaling. They understand how to leverage cloud services effectively while avoiding vendor lock-in and managing costs. Their designs handle node failures and network partitions without data loss.
Phase Allocation
The Data Engineer begins with partial involvement during Alpha phase for initial architecture design. Engagement increases through Beta and Gamma phases as data pipelines are built and optimized. The role maintains significant involvement through Delta and Full Release to handle production data volumes and implement analytics capabilities.
| Phase | Weekly Rate | Allocation | Duration |
|---|---|---|---|
| Alpha | $2,125/week | 25% | 10 weeks |
| Beta | $6,375/week | 75% | 12 weeks |
| Gamma | $8,500/week | 100% | 8 weeks |
| Delta | $8,500/week | 100% | 10 weeks |
| Full Release | $6,375/week | 75% | 12 weeks |
Deliverables
Data Pipeline Architecture - Comprehensive design documentation for end-to-end data flow from edge devices through analytics platforms. This architecture defines processing stages, data formats, and integration points while ensuring scalability and reliability.
Stream Processing Implementation - Production-ready streaming data pipelines that handle real-time event processing with sub-second latency. These implementations include error handling, backpressure management, and exactly-once processing guarantees.
Data Warehouse Design - Optimized warehouse schemas supporting both operational queries and complex analytics with appropriate indexing and partitioning strategies. These designs balance query performance with storage efficiency.
ETL Workflows - Robust ETL processes with automated testing, monitoring, and error recovery capabilities. These workflows ensure data quality while handling edge cases and system failures gracefully.
Data Quality Framework - Comprehensive data quality monitoring including validation rules, anomaly detection, and reconciliation processes. This framework ensures data reliability throughout the pipeline.
API Specifications - Well-documented data access APIs that enable secure, efficient data retrieval for various consumer applications. These specifications include rate limiting, authentication, and usage examples.
Performance Benchmarks - Detailed performance testing results demonstrating system capacity, latency characteristics, and scaling behavior. These benchmarks validate that pipelines meet production requirements.
Monitoring Dashboards - Comprehensive observability dashboards tracking pipeline health, data quality metrics, and system performance. These tools enable proactive identification and resolution of data issues.
Success Criteria
Pipeline Reliability - Data pipelines achieve 99.9% uptime with zero data loss during competition events. Automated recovery mechanisms handle failures without manual intervention.
Processing Latency - End-to-end data latency from event generation to analytics availability remains under 5 seconds for real-time streams. Batch processing completes within defined SLA windows.
Data Quality - Less than 0.1% data quality issues reach production analytics with automated detection and correction for common anomalies. Data validation catches issues before they impact downstream systems.
Scalability Validation - System successfully handles 10x expected peak load during stress testing without degradation. Horizontal scaling demonstrations show linear performance improvements.
Integration Success - Seamless data flow between squat tracking system and existing HYROX infrastructure with zero integration failures during events. APIs maintain backward compatibility through version evolution.
Analytics Enablement - Data warehouse supports complex analytical queries with sub-second response times for common patterns. Athletes and coaches can access insights within minutes of performance completion.