Machine Learning Data Engineer

Selby Jennings; Bethesda (MD); On-site; USD 110,000 - 140,000; Full time

Job summary

Selby Jennings is seeking a hands-on Machine Learning Data Engineer to design and build a production-grade, Snowflake-centric data platform. In this builder role, you'll own end-to-end data pipelines and collaborate with various stakeholders to deliver reliable ML-ready datasets.

The ideal candidate has 5+ years of experience in data engineering, strong Snowflake skills, and proficiency in Python and SQL. You will play a pivotal role in supporting analytics and machine-learning initiatives.

Qualifications

• 5+ years’ experience in data engineering or ML data engineering roles.
• Strong, hands-on Snowflake experience.
• Advanced Python and SQL skills.
• Experience with data modelling for analytics and ML use cases.
• Familiarity with orchestration tools like Airflow or Dagster.
• Experience working in production, high-accountability environments.
• Strong communicator, comfortable working cross-functionally.

Responsibilities

• Design, build, and operate scalable data pipelines ingesting data from internal systems and APIs.
• Own and evolve a Snowflake-based warehouse/lakehouse.
• Implement ELT processes for trusted datasets.
• Build and maintain ML-ready datasets and feature pipelines.
• Support batch and near-real-time data workflows.
• Ensure data quality and reliability.
• Apply best practices around data governance and documentation.
• Partner with analytics and business teams to translate requirements.
• Continuously improve performance and cost efficiency.

Job description

We are hiring a hands‑on Machine Learning Data Engineer for a boutique asset management company to design, build, and scale a production‑grade, Snowflake‑centric data platform that powers analytics and machine‑learning use cases across the firm.

This is a builder role. You will own data pipelines end‑to‑end and work closely with technology, analytics, and business stakeholders to deliver reliable, well‑governed, ML‑ready datasets.

Key Responsibilities

• Design, build, and operate scalable data pipelines ingesting data from internal systems, APIs, and external providers
• Own and evolve a Snowflake‑based warehouse / lakehouse, including schema design, transformations, and optimisation
• Implement ELT processes to create trusted datasets for analytics and machine learning
• Build and maintain ML‑ready datasets and feature pipelines supporting experimentation and production models
• Support batch and near‑real‑time data workflows
• Ensure data quality, freshness, and reliability through monitoring, validation, and alerting
• Apply best practices around data governance, access control, and documentation
• Partner with analytics and business teams to translate requirements into durable data products
• Continuously improve performance, scalability, and cost efficiency

Required Experience

• 5+ years’ experience in data engineering or ML‑data engineering roles
• Strong, hands‑on Snowflake experience (production usage, not exposure)
• Advanced Python and SQL
• Experience with data modelling for analytics and ML use cases
• Familiarity with orchestration tools (e.g. Airflow, Dagster)
• Experience working in production, high‑accountability environments
• Strong communicator, comfortable working cross‑functionally

Highly Preferred

• Exposure to Databricks / Spark
• Background in financial services or regulated data
• Experience in lean, execution‑focused teams

This Role Is Not

• A pure Data Scientist or ML research role
• A BI‑only or reporting‑focused data role
• A DevOps / cloud infrastructure role