YOUR CHALLENGE:
Design, build and own real-time and batch pipelines that ingest clicks, server-to-server (S2S) postbacks, operator data feeds and (where relevant) on-chain events.
Build the canonical, append-only, identity-resolved event model that powers attribution, qualification, commission and reconciliation.
Implement data quality, deduplication/idempotency, reconciliation and observability so figures are trustworthy enough to settle real money on.
Model data and a transformation/semantic layer (e.g. dbt) that powers self-serve analytics and BI for a small, fast-moving team.
Partner closely with product and backend engineering on event instrumentation, tracking links/postbacks and schema design.
Stand up and steward the cloud data platform - infrastructure-as-code, CI/CD, monitoring, cost and security.
Enable downstream measurement and ML use cases — fraud detection, predicted player LTV, benchmarks - by building the data foundations they depend on.
Set engineering standards and mentor as the data team grows.
TO DO IT, YOU WILL NEED:
5+ years in data engineering, including designing and operating scalable production pipelines - both batch and real-time.
Hands-on streaming / event-driven experience (not only batch DWH/ETL) - message buses and stream processing.
Expert SQL and strong Python; solid software-engineering practice (version control, CI/CD, testing, code review).
Proven data modelling (event-based and dimensional) and building a transformation/semantic layer.
Production experience on a cloud data platform with a lakehouse/warehouse and pipeline orchestration.
A data-quality mindset: idempotency, reconciliation, lineage and observability as first-class concerns.
Ownership and pragmatism - comfortable with ambiguity, greenfield builds and wearing several hats in a small fast-paced team.
Excellent communication; works fluently with product, engineering and commercial stakeholders.
BONUS POINTS (DESIRABLE):
Domain experience in iGaming, adtech/martech, affiliate marketing, attribution or payments.
Worked with tracking, attribution, identity resolution or clickstream/event analytics at scale.
Familiarity with crypto / on-chain data.
BI tooling (Power BI, Looker, Tableau, Qlik) and enabling self-serve analytics.