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Contract Data Engineer

United States

Contract Duration: 6 weeks
Hours: Part-time, 15–20 hours/week
Start Date: ASAP
Reports To: Chief Data Scientist, Backstroke.com

About the Role

Backstroke.com is seeking a part-time contract Data Engineer to support critical data engineering work powering our predictive modeling efforts. In this 6-week engagement, you’ll help bring raw data into production-grade pipelines, improve data reliability and observability, and help maintain a large-scale dataset used for machine learning and embedding-based predictive models.

This role is hands-on and execution-focused, working closely with the Chief Data Scientist to accelerate modeling throughput and strengthen the stability and usability of our data foundation.

Key Responsibilities

  • Ingest raw data into production data pipelines used for data science modeling (batch and/or near real-time as needed)

  • Build and enhance AWS-based data workflows, leveraging best practices for scalability and security

  • Set up alerts and notifications in AWS to monitor pipeline health, failures, latency, and data quality issues

  • Create and manage a database layer that stores transformed data, including embeddings used for predictive models

  • Support management of a large-scale dataset, including movement, cleaning, normalization, and maintaining consistency for modeling use

Required Qualifications

  • Strong experience as a Data Engineer supporting machine learning or data science teams

  • Deep working knowledge of AWS services, such as (or similar):

    • S3, IAM, Lambda, CloudWatch, SNS, EventBridge

    • Glue, ECS/EKS, Step Functions (nice to have)

  • Experience building data pipelines (e.g., Python, SQL, Spark, dbt, Airflow, Dagster, Prefect, or similar tools)

  • Experience designing and maintaining databases for ML workflows, including embedding stores and feature-like datasets

  • Comfort working with large datasets and ensuring performance, reliability, and correctness

  • Ability to work independently, communicate clearly, and deliver quickly in a contractor environment

Preferred / Nice-to-Have

  • Familiarity with vector databases and embedding storage patterns (e.g., pgvector, OpenSearch, Pinecone, FAISS, etc.)

  • Exposure to MLOps concepts (feature pipelines, training dataset versioning, model monitoring)

  • Experience with data quality tooling (e.g., Great Expectations, Monte Carlo, custom checks)

Deliverables & Outcomes (6-Week Goals)

  • Reliable ingestion of raw data into modeling pipelines

  • Monitoring and alerting for critical pipeline workflows in AWS

  • Operational database/storage system for embedding-ready transformed data

  • Improved processes for handling and cleaning a large dataset used in predictive models

  • Clear documentation of pipeline architecture and handoff notes for the internal team

Working Style

You’ll collaborate directly with the Chief Data Scientist and contribute to a fast-moving, data-driven team. We value pragmatic engineering, clear documentation, and systems that are reliable and easy to operate.

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