What You'll Do

• Implement new ingestion sources end-to-end against a senior engineer's design — connector code, DAG, schema, monitoring, and catalog registration — extending the team's existing framework where a new source needs a pattern it doesn't yet support
• Investigate pipeline failures independently, recognize known classes of issues, and ship the documented fix without needing to escalate
• Before shipping a new pipeline, identify downstream consumers and what would break if data were late or wrong, and flag gaps like this during spec review — before writing code
• Write clear handovers when escalating a genuinely unresolved issue — what you tried, what you ruled out, and where things diverge — so a senior can pick up without re-discovery
• Review peers' pipeline PRs and catch non-obvious issues (e.g., missing idempotency checks, race conditions) that could cause incorrect downstream data
• Turn ambiguous "why is this data wrong?" questions into structured investigations — tracing data lineage from source to warehouse and communicating back what you found
• Surface concerns in spec review as specific, well-reasoned questions rather than staying silent or blocking progress
• Support data security and governance work (e.g., PII masking, access controls) and contribute to data delivery work, including reverse ETL integrations
• Build strong working relationships with internal stakeholders and help scope and clarify requirements for new work
• Mentor DE2s on their first significant projects — pairing on tricky decisions and helping them apply team conventions

What We're Looking For

• 3–5 years of professional software or data engineering experience
• Strong SQL and Python skills, with solid experience building and operating production data pipelines
• Comfort investigating and root-causing pipeline issues independently before escalating
• Experience with workflow orchestration tools (Airflow or similar) and a cloud data warehouse/lakehouse (Snowflake, Databricks, or similar)
• Solid understanding of data pipeline concepts: idempotency, schema evolution, backfills, and data quality/testing
• Experience giving substantive code review feedback, not just style or formatting comments
• Strong communication skills — can write a clear technical handover, ask sharp questions in spec review, and explain a data lineage investigation to a non-technical stakeholder
• A track record of taking ownership of known-class problems end-to-end rather than needing step-by-step direction

Nice to Have

• Experience extending or building reusable pipeline frameworks/templates
• Exposure to reverse ETL tools or patterns, PII masking, or data access governance (RBAC)
• Exposure to observability/monitoring tooling (e.g., Datadog) for pipeline health and alerting
• Some experience mentoring or informally supporting more junior engineers
• Background in gaming, betting, e-commerce, or another regulated/high-compliance industry

Why Join Us

• Real ownership over known-class problems, with senior/staff support available for the genuinely novel ones
• Work on high-visibility, high-trust systems that the business depends on
• A culture built around clear tenets: standardize before you scale, own the outcome (not just the ticket), and clarity over complexity
• Clear growth path into Senior Data Engineer, with room to start mentoring and shaping team practices along the way

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