Senior Analyst, Data Engineering

**Sr. Analyst, Data Engineering**Data Engineering is the practice of designing, building, and maintaining systems that collect, store, process, govern, and analyze large volumes of data required by analysts, data scientists, and AI/ML applications. It serves as the foundation for enabling data-driven insights, intelligent automation, and AI-powered decision-making across the organization.What You'll AchieveData Management, Engineering & AI Enablement – ETL/ELT processing, data transformation, data quality, governance, security, and AI-ready data architectures across enterprise platforms. You will help enable trusted, high-quality data to support analytics, reporting, machine learning, agentic AI solutions, and operational decision-making. ### Responsibilities **You Will:****Data Migration & Mapping**: Analyze source and target database structures, identify data dependencies, constraints, and transformation needs, and create source-to-target mapping documents with defined transformation rules and business logic in collaboration with stakeholders**Data Pipeline Design & Architecture**: Work with structured and unstructured data to design and implement scalable data pipelines that support analytics, AI, and machine learning workloads, aligning schemas, relationships, and data models with data architects**Data Quality & Governance**: Develop processes to improve data quality, observability, lineage, and governance while ensuring data platforms comply with enterprise security, privacy, and responsible AI standards**AI/ML Enablement & Agentic AI Support**: Partner with data scientists, AI engineers, and business teams to enable trusted datasets for AI/ML model development and support implementation of data solutions for Agentic AI use cases**AI-Driven Development & Automation**: Leverage AI-assisted development tools to improve productivity and documentation quality, and evaluate opportunities for intelligent automation using AI and machine learning techniques within data engineering processes ### Qualifications **Essential Requirements:** **Databases & SQL**: Proficiency in Teradata, PostgreSQL, and SQL for querying, transforming, profiling, and validating data, with a strong understanding of relational, dimensional, and analytical data models to accurately map source-to-target schemas**ETL/ELT & Development Practices**: Experience with Informatica, Apache Airflow, or comparable data integration platforms, along with familiarity with version control and CI/CD practices using Git-based development workflows**Programming & Automation**: Proficiency in Python for automation, orchestration, and custom data solutions, with the ability to manage unexpected data quality issues, platform constraints, and migration challenges with agility**Data Quality & Governance**: Understanding of data lineage, metadata management, master data management (MDM), and governance concepts to ensure data integrity and compliance throughout the data lifecycle**AI-Ready Data Engineering**: Knowledge of data preparation, feature engineering concepts, and dataset management for AI/ML workloads, enabling trusted and well-governed datasets for advanced analytics and model development**Desirable Requirements:** **Analytics, Reporting & Modern Data Platforms**: Knowledge of Power BI or other visualization tools, experience with Airflow, enterprise schedulers, SSIS, SSRS, and Tabular OLAP/semantic modeling, along with understanding of MLOps, DataOps, cloud-native data services, modern lakehouse architectures, and data observability/automated anomaly detection solutions **AI & Intelligent Automation**: Exposure to machine learning and AI technologies for automation and operational efficiency, including familiarity with Agentic AI concepts (LLMs, prompt engineering, vector databases, semantic search, responsible AI/AI governance), experience using AI-powered productivity tools such as GitHub and Devin, and understanding of modern AI-driven development practices **Comp…