Sr. Data Engineer - INTL

Job Description We are seeking a highly skilled Data Engineer to support the design, development, and implementation of an enterprise-grade data catalog, governance framework, and data product ecosystem for Symphony data sources. This role will play a critical part in enabling end-to-end data product development, data discoverability, quality, lineage, and AI-driven operations using Microsoft Fabric and Microsoft Purview. Key Responsibilities 1. Data Catalog Development Scan, onboard, and manage prioritized Symphony data sources within Microsoft Purview Data Catalog Curate and maintain catalog assets, including: Business definitions and metadata standardization Business glossary creation and extension Alignment of metadata across new and existing datasets Ensure high-quality, searchable, and governed catalog entries for enterprise use 2. Data Product Development & Enablement Design and develop end-to-end data products for Symphony datasets Build and manage storage architectures using Microsoft Fabric (OneLake, Lakehouse, Warehouse) Publish certified, production-ready data products and associated metrics metadata Ensure alignment with enterprise governance standards and domain ownership models 3. Data Modeling & Semantic Layer Design and implement data models and entity relationships aligned to business domains Develop semantic models for downstream analytics and reporting consumption Standardize enterprise dimensions and metrics (e.g., Brand, Country, KPIs) Support consistent and reusable data definitions across domains 4. Ontology & Relationship Graph Enablement Define and implement ontology frameworks using entity models Build relationship graphs within Microsoft Fabric to support: Data quality analysis Cross-domain insights Enable relationship-aware data structures for improved analytics and AI use cases 5. Data Quality & Profiling Implement and scale data profiling across Symphony datasets Extend existing Data Quality (DQ) frameworks to new data sources Develop and publish data quality dashboards, reports, and metrics Build automated DQ monitoring solutions and AI-driven data quality agents 6. Data Lineage & Governance Establish end-to-end data lineage across ingestion, transformation, and consumption layers Ensure full traceability from source systems to data products Implement and enforce governance standards including: Data classification and sensitivity labeling Ownership and stewardship assignment Role-based access controls and compliance alignment Partner with stakeholders to integrate into enterprise governance frameworks 7. AI-Driven Data Capabilities Design and implement AI-powered agents and automation to support: Data quality monitoring and anomaly detection Metadata curation and recommendations Data discovery and query assistance Leverage built-in AI capabilities within Microsoft Fabric and Purview Continuously optimize processes through intelligent automation We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.

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https://insightglobal.com/workforce-privacy-policy/. Skills and Requirements

• 7+ years experience in data engineering or data platform development
• Microsoft Fabric (OneLake, Lakehouse, Warehouse)
• Microsoft Purview (Data Catalog, Governance, Lineage)
• Data modeling (dimensional and relational)
• Metadata management and data governance
• ETL/ELT pipelines and distributed data processing
• Experience implementing data quality frameworks and monitoring solutions
• Proficiency in SQL and modern data engineering tools/languages (Python, Spark, etc.

) Experience building data products in enterprise environments Familiarity with ontology modeling and graph-based data structures Exposure to AI/ML-driven data management or automation use cases Experience working in regulated or federal environments Knowledge of data governance frameworks (e.g., DAMA, DCAM)