About the Role
Data is the substrate every AI agent we ship runs on. As a Cloud Data Engineer, you will build the pipelines, models, and storage that make customer context fast, correct, and trustworthy — for both BI and AI workloads.
You will write SQL that holds up under scrutiny, ship Spark jobs that finish, and design data layouts that other engineers and agents can reason about. This role is for someone who cares about correctness, schema sanity, and runtime cost — not just "moving data from A to B."
What You'll Do
- Model and ship SQL across lakehouses and warehouses for analytics, BI, and AI.
- Build ETL / ELT pipelines on Spark — batch and incremental, with sensible partitioning and schema evolution.
- Stand up AWS Athena queries and external tables over S3 data lakes.
- Curate DWH layers — raw, staged, conformed, marts — with contracts and tests.
- Wire pipelines into RAG, text-to-SQL, and agent workflows.
- Own data quality, observability, and runbooks for the pipelines you ship.
Required Skills
- SQL — joins, window functions, CTEs, query plans.
- DWH — dimensional modeling, SCDs, warehouse vs lakehouse trade-offs.
- Spark — PySpark or Spark SQL; debug skew, ship a transformation.
- AWS Athena & S3 — partitioned tables, Parquet, lake hygiene.
- Python — working level for pipelines and utilities.
- Engineering habits — testing, review, docs; curiosity about cost.
Nice to Have
- Iceberg or Delta Lake table formats.
- Airflow / dbt / similar orchestration.
- Streaming pipelines — Kinesis, Kafka.
- Open-source or competition data work.
- Embeddings, vector stores, or RAG pipelines.
Qualifications
- Bachelor's / Master's in CS, Data Engineering, Information Systems, or related — or equivalent engineering experience.
- 0–3 years of professional or substantial project experience in Data Engineering.
- Comfortable in a startup environment — high ownership, ambiguity, fast iteration.
What We Offer
- Real data and AI problems with paying customers.
- Mentorship from senior engineers (ex-AWS, ex-Google, ex-IBM).
- A culture where data quality and runtime cost are first-class.
- Competitive compensation, learning budget, rapid growth path.
- Direct exposure to architecture decisions from day one.
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Cloud Data Engineer
Send your application
Tell us about you, share your GitHub or portfolio, and add a short note on a data pipeline you have built or contributed to — what you owned, what you would do differently.