Data Engineer

About the Role

We build systems that run on data, and that data has to be ingested, transformed, and validated before anything downstream can trust it. As a Data Engineer, you build the ingestion and transformation pipelines, and the quality checks that keep them honest.You'll work inside a Monks delivery team, alongside clients, engineers, and product, owning the pipelines the rest of the product is built on.We're all-in on agentic engineering. Tools like Claude Code and Codex are part of our everyday workflow, and we're looking for people who are genuinely fluent working alongside AI coding agents and excited to push those practices further.

Who You Are

Rigorous about correctness: you assume data is dirty until your checks prove otherwise.

Ownership-minded: you keep the pipelines you build reliable and observable.

A pragmatic problem-solver: you fix the root cause, not just the symptom.

A clear communicator: you make data quality legible to non-specialists.

What You'll Do

Build and maintain ingestion and transformation pipelines across diverse sources.

Build automated validation and integrity checks that catch problems early.

Model and move data so downstream systems can rely on it.

Monitor pipeline health and troubleshoot data issues.

Review code, including AI-generated code, for quality and consistency.

Collaborate with engineers and product to define quality standards.

What You Bring

5+ years in data or backend engineering.

Language-flexible: we work across TypeScript / Node and Python, and you're strong in at least one.

Strong SQL and data modeling across relational and analytical stores.

Solid grasp of ingestion, ETL, and pipeline architecture.

Experience designing data validation and quality checks at scale.

Experience building on AWS.

Strong English communication skills.

Comfortable in a fast-paced, agile consulting environment.

Extra Credit

Distributed transformation frameworks (e.g. PySpark, Snowpark).

dbt, Snowflake, or modern warehouse experience.

Streaming and real-time pipelines.

Working with documents or other unstructured data.

Open-source work you're proud to link.