Senior Azure Data Platform Engineer

Role Overview

We are seeking a senior-level Azure Data Platform Engineer with deep expertise in Azure SQL Managed Instance, Azure Data Factory (ADF), and enterprise database administration. This individual will play a hands-on leadership role in managing, troubleshooting, and driving data platform reliability and performance across a complex client environment.

This is not a passive support role. The ideal candidate is someone who will step in, take ownership of data pipelines and database operations, and proactively identify and resolve issues—often in high-pressure or ambiguous situations with demanding stakeholders.

This role is best suited for a practitioner who can balance deep technical execution with client engagement, operate across vendor ecosystems, and lead critical data loading and platform operations end-to-end.

Engagement Focus

• Long-term contract supporting a mission-critical data platform
• Emphasis on stability, performance improvement, and operational ownership
• Requires a resource who can operate independently and lead through ambiguity
• Not advisory—this is a hands-on execution and delivery role

Key Responsibilities

Azure SQL Platform Administration & Optimization

• Administer and manage Azure SQL Managed Instance and Azure SQL databases in production environments
• Perform database performance tuning, including indexing strategies, query plan analysis, and workload optimization
• Diagnose and resolve locking, blocking, and concurrency issues
• Implement and manage database security, user access, and role-based permissions
• Ensure robust backup, recovery, and high availability practices

Data Factory (ADF) Engineering & Operations

• Design, build, and maintain ADF pipelines for batch and incremental data loading
• Administer ADF environments, including monitoring, scheduling, and failure handling
• Troubleshoot pipeline failures, integration issues, and performance bottlenecks
• Analyze execution logs and metrics to identify systemic issues in data processing workflows
• Ensure pipelines are resilient, scalable, and operationally supportable