Senior Database Engineer

Responsibilities

• Design, build, and maintain enterprise-grade data warehouses within Microsoft Fabric (Warehouse, Lakehouse) using PySpark.
• Implement and manage data models including fact and dimension tables supporting operational and analytical workloads.
• Apply Medallion architecture (Bronze, Silver, Gold) to structure scalable, maintainable data pipelines.
• Optimize storage, query performance, and cost efficiency across Fabric and Azure environments.
• Develop, deploy, and maintain data pipelines using PySpark, SQL, and Azure-native services to facilitate development across the Dev Ops team.
• Engineer scalable ETL/ELT processes that ingest, transform, and load data across multiple systems.
• Own pipeline reliability, monitoring, alerting, and failure recovery in production environments.
• Troubleshoot and resolve issues across ingestion, transformation, and storage layers with minimal escalation support.
• Design and manage CI/CD pipelines in Azure Dev Ops for database changes, data pipelines, and data models.
• Implement version control, automated deployments, and testing strategies for all data assets.
• Maintain release processes for schema changes, stored procedures, and pipeline updates across environments.
• Write, optimize, and maintain advanced T-SQL code (stored procedures, views, functions, indexing strategies).
• Perform query tuning and performance optimization across high-volume transactional and analytical workloads.
• Design and enforce database standards, naming conventions, and documentation practices.
• Lead the Database Administrator in best practices around Data governance, reliability and security.
• Implement role‑based access controls and data security aligned with HIPAA and internal policies.
• Own backup, restore, and disaster recovery strategies for databases and pipelines.
• Conduct regular validation of data integrity and pipeline outputs.
• Partner with analysts, operations, and engineering teams to translate business requirements into scalable data solutions.
• Provide technical guidance on data modeling, pipeline design, and platform best practices.
• Document systems, pipelines, and processes to ensure maintainability despite lean operational support.

• 5+ years of experience in a Database Engineer, Data Engineer, or similar role owning database and pipeline development in production environments.
• 3+ years of hands‑on experience working with PySpark in distributed data processing environments.
• 3+ years of experience building and maintaining data platforms in Microsoft Fabric or comparable modern data platforms (e.g., Synapse, Databricks).
• 5+ years of advanced T-SQL experience, including performance tuning, indexing, and complex query development.
• 3+ years of experience managing CI/CD pipelines in Azure Dev Ops (ADO) for database and data pipeline deployments.
• 3+ years of experience managing or developing enterprise data warehouses, including dimensional modeling and large‑scale data transformations.
• 2+ years of experience operating and supporting high‑impact production data pipelines with limited oversight or support.
• Technical Skills
  • PySpark & Python (required)
  • T‑SQL (required)
  • Microsoft Fabric (required)
  • Azure Dev Ops (CI/CD pipelines)
• Experience with:
  • Azure data services (Azure SQL, Fabric, Data Factory, Logic Apps, Functions)
  • ETL/ELT pipeline design patterns
  • Data modeling methodologies (Kimball, Medallion)
  • Microsoft PowerBI dashboard builder
  • Familiarity with DAX for analytical modeling
  • Scripting languages (Python, Power Shell)