Data Engineer

Responsibilities

• Build and expose the data lake/lakehouse so teams can understand business and product performance and make better decisions.
• Design and evolve a data platform that enables scientists and analysts to query, model, and ship features and product metrics.
• Develop scalable and reliable batch and streaming pipelines that ingest from varied sources and land trusted datasets.
• Maintain and fix existing ETLs, improving reliability, observability, and cost while planning safe backfills and replays.
• Model curated layers and maintain transformation logic (e.g., dbt ), including testing, documentation, and data quality.
• Build with Infrastructure as Code (e.g., Terraform ) and contribute to CI/CD and deployment practices.
• Partner with Engineering, and Data Science to deliver low-latency signals and features for real-time decisioning.
• Contribute to internal tools, reusable components, and simple dashboards that unblock self-service.

Requirements

• Solid engineering foundation with 5+ years of relevant experience in data engineering and/or distributed systems.
• Hands-on work building data pipelines (batch and/or streaming) using at least one distributed framework (e.g., Spark, Kafka, Flink ).
• Comfortable with SQL and a transformation framework (e.g., dbt or similar).
• Familiarity with AWS services for data (any mix of S3, EMR/Glue, Kinesis/MSK, Lambda, Redshift/Athena) or equivalent cloud experience.
• Exposure to workflow orchestration (e.g., Airflow).
• Experience using Terraform (or similar IaC) to manage data/platform resources.
• Ownership mindset, focus on reliability, and enjoyment of collaborating in a fast-moving environment.

What are we trying to solve?