Key Responsibilities

Lead the Architectural Redesign: Analyze and re-architect the existing highly-normalized PostgreSQL database, driving the transition to a modern, high-performance analytical data model.

Optimize for Performance: Redesign schemas, balancing normalization with analytical read efficiency. Strategically apply denormalization, materialized views, and dynamic queries to support high-speed data access.

Scale Data Delivery: Introduce advanced PostgreSQL strategies including robust indexing, partitioning, caching, and replication to ensure high-throughput and low-latency access for client-facing APIs and internal tools.

Data Pipeline Excellence: Design, implement, and maintain scalable data pipelines and ETL/ELT frameworks across distributed systems to ensure data reliability and freshness.

Technical Leadership: Act as a subject matter expert, guiding the engineering team on best practices for data modeling, query optimization, and architectural decisions.

Skills & Experience

5+ years of experience as a Senior Data Engineer, Database Architect, or equivalent role in high-scale, read-heavy data systems.

Expert-level proficiency in PostgreSQL schema design, optimization, and performance tuning (e.g., query planning analysis, configuration tuning).

Proven track record of successfully re-architecting complex, highly normalized OLTP systems into efficient OLAP/analytical data warehouses.

Deep practical knowledge of advanced database concepts including indexing strategies, partitioning, caching mechanisms, and replication patterns.

Strong experience with building and managing data ingestion pipelines and ETL/ELT processes in a modern cloud environment.

Exceptional communication and collaboration skills. Must be proactive, articulate, and comfortable managing technical scope and expectations with leadership and product stakeholders.

Nice to Have

Prior professional experience working with data systems in sports analytics, betting, or digital media.

Experience operating within a globally distributed team spanning the US and UK time zones.

A pragmatic, hands-on approach: a willingness to dive into legacy schema cleanup while simultaneously designing future-state systems and leading by example.