Date Engineer Datawarehouse ETL ELT

• Design, build, and maintain ETL/ELT pipelines using Microsoft Fabric (Pipelines, Dataflows Gen2, Notebooks, Spark) and legacy SSIS.
• Develop ingestion frameworks for flat files (CSV/Excel), APIs, SaaS platforms, cloud feeds, and partner data.
• Implement medallion architecture (Bronze, Silver, Gold) using Lakehouse (Delta Lake), Warehouse, and OneLake.
• Automate data transformations using SQL, PySpark, and Fabric Notebooks.

Data Modeling & Optimization

• Build and optimize star schema models, conformed dimensions, and fact tables for BI consumption.
• Implement incremental loads, SCD handling (Type 1/2), partitioning, Z-ordering, compaction, and other Delta Lake optimization techniques.
• Collaborate with BI Analysts to translate business requirements into performant data models., * Design, build, and maintain ETL/ELT pipelines using Microsoft Fabric (Pipelines, Dataflows Gen2, Notebooks, Spark) and legacy SSIS.
• Develop ingestion frameworks for flat files (CSV/Excel), APIs, SaaS platforms, cloud feeds, and partner data.
• Implement medallion architecture (Bronze, Silver, Gold) using Lakehouse (Delta Lake), Warehouse, and OneLake.
• Automate data transformations using SQL, PySpark, and Fabric Notebooks.

Data Modeling & Optimization

• Build and optimize star schema models, conformed dimensions, and fact tables for BI consumption.
• Implement incremental loads, SCD handling (Type 1/2), partitioning, Z-ordering, compaction, and other Delta Lake optimization techniques.
• Collaborate with BI Analysts to translate business requirements into performant data models.

Data Quality, Governance & Security

• Ensure end-to-end data quality through validation, reconciliations, profiling, and automated tests.
• Apply governance principles using Purview for lineage, classification, and data cataloging.
• Enforce Row-Level Security (RLS), object-level security, and access controls across Fabric datasets.

Cross-Team Collaboration

• Partner with BI Analysts and Business Stakeholders to understand KPIs, metrics, and reporting requirements.
• Work with Architects to establish data platform standards, naming conventions, folder structures, and version control patterns.
• Provide technical expertise during UAT, troubleshooting, and performance tuning.

Operational Excellence

• Monitor pipeline performance and proactively resolve pipeline failures.
• Implement CI/CD practices using Azure DevOps / Git integration for code and artifact promotion across Dev, Stage, and Prod.
• Contribute to documentation of data flows, data dictionaries, technical specifications, and workflows.

Must be strong in Data Warehouse development/ETL/ELT and be able to create a DW from the bottom up., * Bachelor's degree in Computer Science, Information Systems, Engineering, or related field.

• 9+ years of experience in data engineering, BI development, or data warehouse development.
• Strong SQL skills (T-SQL) for complex transforms, joins, window functions, and performance tuning.
• Hands-on experience with Microsoft Fabric (Lakehouse, Warehouse, OneLake, Pipelines, Dataflows Gen2, Notebooks).
• Experience with Delta Lake, parquet, and medallion architectures.
• Proficiency with Python or PySpark for ingestion and transformation.
• Experience integrating REST APIs, SFTP feeds, SaaS connectors, and partner files.
• Strong understanding of dimensional modeling (Kimball), conformed dimensions, and data mart design.
• Familiarity with CI/CD workflows (Azure DevOps, Git).
• Excellent troubleshooting, debugging, and performance optimization abilities.
• 5 years of experience with SSMS / SSDT / SSIS / SSAS / SSRS., * Experience with Power BI (understanding semantic models and performance considerations).
• Exposure to Azure Data Factory, Synapse, or Databricks.
• Experience with workflow orchestration and metadata-driven frameworks.
• Knowledge of data governance tools (Purview), data security best practices, and lineage management.