Data Engineer

You'll design, build, and own data pipelines and integrations end-to-end, new customer sources, new connectors, and the transforms and data models behind them. We're looking for someone who takes full ownership of what they build and is a strong prospect to grow into a senior data engineer in the near future.

Responsibilities:

• Build new integrations end-to-end: source connectors (SaaS APIs via Nango, API-key sources via Python clients, and warehouse sources), the extract/transform/load code, and the data models that land clean data into our ClickHouse silver/gold tables. You own your pipelines from the first line to production.
• Design transforms and data models for the sources you own: deciding how a new customer's data maps into our shared schema, not just implementing someone else's spec.
• Write clean, tested, idempotent pipeline code to our engineering standards: unit tests, schema/data-quality validation, structured logging, and docstrings.
• Onboard new customers: bring up their sources, map their entities, and solve the data-quality and modelling quirks that make every customer different.
• Own the operability of what you build: freshness checks, backfills, and fixing your pipelines when they break (because the person who built it fixes it best).
• Contribute to the shared platform and to code reviews; write the runbooks and data dictionaries for the pipelines you own.

Requirements:

• 2-3 years of professional data engineering (or backend engineering with substantial data work).
• Strong, clean, typed, testable code; comfortable with pandas/polars or equivalent for transforms.
• Solid SQL and understanding of relational/columnar data modeling; able to write and reason about non-trivial queries.
• Experience building ETL/ELT pipelines and consuming REST APIs (auth, pagination, rate limits, retries).
• Familiarity with cloud data infra: AWS (S3 especially), containers/Docker, and a warehouse or OLAP store (ClickHouse, BigQuery, Snowflake, Redshift, or similar).
• Version control (Git) and working within a PR/code-review flow.
• Nice-to-have: ClickHouse specifically; dlt/Airbyte/Fivetran; Dagster/Airflow; dbt.
• Experience with multi-tenant data or ingestion from third-party/partner APIs.
• Exposure to data-quality tooling (Great Expectations, dbt tests) or testing frameworks (pytest).

Traits and soft skills:

• Ownership: you finish what you pick up, including the unglamorous parts (tests, docs, monitoring).
• Curiosity and willingness to learn: you ask good questions and ramp fast on an unfamiliar stack.
• Attention to detail: you care that row counts reconcile and that bad data doesn't slip downstream.
• Clear communicator: you can explain what broke and what you did, and you write things down.
• Self-starter: you take a problem and drive it to a shipped, working pipeline without needing constant hand-holding.

Skills

Python, SQL, AWS, ELT, ETL