Senior Data Engineer

Design, build, and operate the core data infrastructure: data lake, warehouse, orchestration, observability, and governance, using declarative configuration and infrastructure as code (Terraform or equivalent) so the platform is reproducible and auditable

Partner with platform and domain teams to design ingestion pipelines and implement declarative configuration for data sources across the stack

Architect the transformation layer: dimensional models, aggregation strategies, and incremental materialization patterns that balance query performance against pipeline cost at scale

Own streaming and near-real-time data flows for fraud signal propagation, transaction status events, and verification webhooks, with the reliability expectations those require

Build for scale: partition strategies, clustering, late-arriving data handling, and backfill patterns that hold up when data volume doubles

Own the source-of-truth models for the metrics the business runs on: ARR, NRR, churn, transaction volume, fraud detection rates, customer health scores, and operational throughput

Make the numbers defensible: when a business leader challenges a metric, you can walk them through exactly how it is calculated, what is excluded, and why

Partner with Product, Finance, CS, and GTM to translate business questions into data models and help teams measure what actually matters 

Write production-grade Python and SQL: modular, tested, version-controlled, and reviewable by someone who was not in the room when you wrote it

Implement CI/CD pipelines for data systems: automated testing, schema change detection, data contract validation, deployment gates, and cost optimization and performance tuning as ongoing practice, not one-time projects

6+ years in data engineering with primary, end-to-end ownership of a production data platform, not a supporting role on a large team

Direct experience designing and operating streaming or near-real-time pipelines (Kafka, Kinesis, Pub/Sub, Flink, or equivalent) at production scale, including debugging failures under load

Hands-on production experience with cloud-based data platforms (Snowflake, BigQuery, Redshift, Databricks, or equivalent) and a production-grade orchestrator (Airflow, Dagster, Prefect, or equivalent)

Expert SQL and distributed systems: window functions, recursive CTEs, query plan analysis, query concurrency management, and optimization strategies that go beyond adding an index

Strong Python for data engineering: production-quality pipeline code with error handling, idempotency, retry logic, and test coverage; Go is a meaningful plus

Dimensional modeling mastery: you understand the tradeoffs between normalized and denormalized designs, when SCDs are the right tool, and how incremental strategies affect downstream query semantics

Event-driven architecture fundamentals: exactly-once semantics, consumer group management, backpressure handling, offset management, and the operational realities of keeping a streaming pipeline healthy

Warehouse internals: clustering keys, materialized views, partition pruning, and cost optimization strategies that keep query costs from compounding as data volume grows