ML Data Engineer

Key Responsibilities: Design, build, and maintain scalable data pipelines and data ingestion processes. Integrate data from multiple enterprise sources into an Azure-based data platform. Develop and support automated data processing and transformation workflows. Improve how data is discovered, onboarded, governed, and delivered to users. Prepare and structure data for analytics, machine learning, and AI use cases. Partner with ML, AI, software engineering, and data teams to support new solutions. Support data quality, reliability, governance, and monitoring. Assist with modernization of legacy data processes and movement toward more automated solutions. Participate in troubleshooting, testing, deployment, and ongoing improvement of data solutions.

Qualifications: Required Skills3–5 years of Data Engineering or related software/data development experience. Strong experience with Python and SQL.Experience building ETL/ELT pipelines. Experience working with Azure data technologies. Understanding of data modeling, transformation, and integration. Experience working with APIs, structured and semi-structured data, and multiple data sources. Working knowledge of CI/CD and modern development practices. Ability to troubleshoot data pipeline and data quality issues. Strong communication and collaboration skills.

Preferred: SkillsExposure to machine learning pipelines or MLOps. Experience preparing and managing datasets used by ML models. Familiarity with AI/GenAI applications and how enterprise data supports AI solutions. Experience with data governance or cataloging technologies (e.g., Purview or similar).Experience automating data ingestion and onboarding processes. Familiarity with cloud-based data platforms and enterprise data architecture. Great benefits package & 401k. Perks and incentives. Ongoing support and career coaching.