Senior People Data Engineer

The Senior People Data Engineer designs, builds, and supports modern data solutions that turn complex enterprise data into trusted, scalable, and reusable data products for analytics, reporting, automation, and AI. This role is responsible for integrating data from multiple internal and external sources, building reliable ingestion and transformation pipelines, and preparing curated datasets that are secure, well-documented, and ready for downstream consumption. The role includes a practical focus on Microsoft Fabric and Workday-related data, while remaining broad enough to support wider enterprise data engineering needs. This position requires strong hands-on experience with cloud and enterprise data platforms, pipeline orchestration, lakehouse or warehouse design, data modeling, quality controls, and performance optimization. The Senior Data Engineer works closely with analysts, data scientists, architects, application owners, and business stakeholders to deliver durable data solutions that improve decision-making and enable advanced analytics and AI initiatives. Key Activities and Responsibilities • Design, build, and maintain scalable data pipelines that ingest, transform, validate, and publish data from enterprise applications, APIs, flat files, databases, cloud platforms, and other structured or semi-structured sources. • Develop and support modern data solutions in Microsoft Fabric using pipelines, notebooks, lakehouses, dataflows, warehouses, and related services as appropriate to the use case. • Build and manage reusable data models and curated data layers that support analytics, operational reporting, self-service BI, data science, and AI use cases. • Integrate Workday and Workday Prism data with other enterprise sources to support cross-functional reporting, historical analysis, and downstream data products. • Apply strong data engineering standards for schema design, metadata, lineage, documentation, naming conventions, and change management. • Prepare data for AI and advanced analytics by improving data quality, consistency, context, discoverability, and semantic clarity. • Implement automated data quality checks, reconciliation processes, and exception handling to improve trust in enterprise data assets. • Monitor pipeline health, job performance, refresh reliability, and data latency; troubleshoot root causes and implement durable fixes. • Optimize storage, transformation logic, partitioning, query performance, and compute usage to improve cost, speed, and scalability. • Partner with business stakeholders and technical teams to translate business requirements into practical, maintainable data solutions. • Support secure access to sensitive data by applying governance, privacy, retention, and role-based access standards consistent with company policy and regulatory requirements. • Contribute to platform improvement by evaluating emerging tools, patterns, and features in areas such as data engineering, AI enablement, automation, and observability. Recommended Experience and Qualifications • 5+ years of progressive experience in data engineering, analytics engineering, BI engineering, data architecture, or a closely related technical role. • Strong hands-on experience building and supporting enterprise ETL/ELT pipelines and curated analytical data assets. • Experience with Microsoft Fabric, including one or more of the following: lakehouse, pipelines, notebooks, Spark/PySpark, dataflows, warehouse, semantic models, or related platform administration. • Experience working with Workday data, Workday reporting, Workday Prism Analytics, or similar HCM/ERP data platforms is strongly preferred. • Strong SQL skills and practical experience with Python, PySpark, or similar data transformation and automation tools. • Experience integrating multiple data sources, including APIs, flat files, SFTP-based feeds, operational systems, and cloud or on-premises data platforms. • Experience with data modeling, dimensional concepts, semantic layers, and performance tuning for analytics workloads. • Experience delivering data products that support reporting, advanced analytics, machine learning, natural language interfaces, or other AI-enabled use cases. • Bachelor's degree in Computer Science, Information Systems, Data Engineering, Analytics, or a related field; equivalent practical experience may be considered in lieu of a degree. Communication and Collaboration Skills • Clear verbal and written communication skills, with the ability to explain technical concepts in plain language to non-technical stakeholders. • Strong collaboration skills and the ability to work effectively across analytics, IT, HR, Finance, and business operations teams. • Ability to ask strong discovery questions, challenge unclear requirements, and drive toward well-defined business logic and source-of-truth decisions. • Strong documentation habits, including process documentation, technical specifications, assumptions, and support notes. • Ability to communicate tradeoffs, risks, dependencies, and data limitations clearly and early. • Comfort operating in fast-moving environments where priorities shift and data issues require practical judgment and follow-through. Preferred Technical Skills • Microsoft Fabric • Workday Prism Analytics / Workday reporting • SQL • Python / PySpark • Lakehouse / warehouse architecture • ETL / ELT / orchestration • Data modeling / semantic modeling • Data quality, lineage, governance, and security controls • Power BI or comparable analytics and visualization platforms