Data Engineer- Python, AI/ML

Key Responsibilities

• Build and maintain Python and SQL pipelines for governance-related ingestion, cleaning, transformation, and validation of structured and semi-structured data.
• Implement and operate data quality checks, schema validation, and integrity rules across pipelines; investigate and resolve quality issues.
• Contribute to master data workflows: standardization, deduplication, and consolidation of data from heterogeneous sources into consistent reference and golden-record datasets.
• Instrument pipelines for data lineage, metadata, and catalog tooling.
• Develop pipelines that feed governance dashboards and reporting in Tableau, Power BI, or Looker.
• Build reproducible, well-documented pipelines for compliance and audit reporting.
• Contribute to AI / ML-assisted governance use cases: embedding-based data classification, anomaly detection on quality metrics, LLM-assisted catalog search, and MCP-based exposure of governed datasets to AI assistants.
• Partner with team leads, data stewards, and stakeholders to translate governance requirements into engineering work.
• Follow team engineering practices: Git, code review, modular pipeline design, automated testing, CI/CD.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Statistics, or a related field.
• 2 years building data pipelines in Python (Pandas, NumPy, SciPy) and SQL.
• Working experience with Apache Spark or PySpark and workflow orchestration (Apache Airflow).
• Schema design across relational (PostgreSQL, MySQL, SQL Server) and analytical databases, including standardization across heterogeneous sources.
• Experience implementing data quality validation, EDA, and integrity enforcement on production datasets.
• Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP).
• Working familiarity with Python ML libraries (Scikit-Learn) for feature engineering and exploratory analysis.
• Experience producing analytics-ready datasets for BI tools (Tableau, Power BI, or Looker).
• Git, code review, and CI/CD practices.
• Clear technical communication and collaborative working style.