Deeter Analytics is hiring its first Data Engineer to join a new algorithmic division. In this foundational role, you will own critical datasets end-to-end, designing and running the data backbone that powers quantitative trading. You will work directly with traders and researchers to transform messy external feeds into clean, reliable, and high-performance data products.

What You'll Do

Architect cloud-native batch and streaming ELT for diverse sources; standardize, de-duplicate, and document data; define schemas and redundancy.
Stand up the core data platform including storage, orchestration, metadata catalog, CI/CD, IaC, and observability with a focus on simplicity and cost-awareness.
Implement data quality checks, anomaly detection, and maintain survivorship-bias-free histories while handling corporate actions and entitlements.
Expose clean data via APIs, query layers, and shared libraries to produce 'research-ready' datasets for fast backtests and production.
Partner with quantitative analysts, data scientists, and software engineers to scope, prototype, and productionize new datasets quickly; own incident response and runbooks.
Uphold security and access hygiene including IAM/least-privilege, secrets management, and audit.

What We're Looking For

1+ years building and operating production data pipelines or platforms.
Strong skills in Python and SQL.
Comfortable working on at least one major cloud provider (AWS/GCP/Azure).
Experience with Docker and infrastructure-as-code tools like Terraform.
Experience with orchestration tools such as Airflow, Prefect, or Dagster.
Experience with distributed/batch compute (e.g., Spark/Dask/Beam), data warehouses/lakes, and columnar formats (e.g., Parquet/Delta/Iceberg).
Experience with monitoring/observability (logs, metrics, traces) and cost management.
Proven delivery for quantitative users or ML/DS teams; clear thinking, clean design, and pragmatic trade-offs.

Nice to Have

Financial or time-series data experience (corporate actions, vendor entitlements/licensing), alternative data ingestion.
Multimodal ETL experience (NLP/embeddings, transcription, basic image/video processing).
Experience with dataset/version control and reproducibility (e.g., LakeFS/DVC) and research workflow tooling.