About The Role
The role owns the data infrastructure that powers analytics and product decisions — batch and streaming pipelines, warehouse modeling, and the orchestration layer that keeps hundreds of daily data jobs running reliably.
You will work directly with analytics engineers, data scientists, and backend teams to turn raw event, transactional, and third-party data into modeled, trustworthy datasets that downstream teams build on without hesitation.
Key Responsibilities
- Design, build, and maintain ELT pipelines in Python and SQL, ingesting from sources like Postgres, Kafka, and third-party APIs into a cloud warehouse (Snowflake, BigQuery, or Redshift)
- Develop and enforce dimensional and wide-table data models using dbt, including testing, documentation, and lineage tracking
- Own orchestration and scheduling in Airflow or Dagster, including DAG design, dependency management, and failure recovery
- Build streaming and near-real-time pipelines for event data using Kafka, Kinesis, or Flink where latency matters
- Implement data quality frameworks — freshness checks, schema validation, anomaly detection — with automated alerting to on-call rotations
- Optimize warehouse performance and cost: partitioning strategies, clustering, query tuning, and incremental materialization patterns
- Partner with analysts and product teams to define contracts for core datasets and reduce ad hoc pipeline firefighting
What We Are Looking For
- 3–6 years of experience in data engineering, building and operating production pipelines at meaningful scale
- Advanced SQL and strong Python; deep experience with at least one cloud data warehouse (Snowflake, BigQuery, or Redshift)
- Hands-on production experience with dbt and an orchestrator such as Airflow, Dagster, or Prefect
- Experience with cloud platforms (AWS, GCP, or Azure) and infrastructure-as-code tooling (Terraform preferred)
- Solid grounding in data modeling fundamentals — dimensional modeling, slowly changing dimensions, incremental processing — and distributed data processing (Spark a plus)
- Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience
- Bonus: Experience with streaming architectures (Kafka/Flink), data lakehouse formats (Iceberg, Delta Lake), or building data platforms serving 50+ internal consumers