Software Engineer: Data Pipelines and ML (Mid-career / Senior)

Strong production Python.

Datasets in pandas and Parquet/Arrow, plus an analytical engine, e.g. DuckDB, or a warehouse such as Snowflake.

Orchestrated batch pipelines you've operated, not just written: Dagster or Airflow, S3-based data flows, and a habit of testing outputs for correctness rather than only for exceptions.

Applied ML in production: embeddings and semantic similarity, clustering, or operationalising models (not necessarily training from scratch).

AWS fluency and CI/CD discipline.

Practical LLM engineering: prompting, batch inference, and cost and throughput trade-offs across providers.

SageMaker, Bedrock, or comparable managed ML tooling.

Financial and equities domain knowledge: classification taxonomies, factor models, index construction. Valuable but learnable.

Infrastructure as code (AWS CDK or Terraform) and Docker.

We care more about what you've owned than years on a CV. If you've built a pipeline that runs on a schedule against real volume, and you were the person who got paged when it broke, you're in scope. More senior candidates will typically have made the cost and throughput trade-offs, and set the standard for how a team tests data quality.