Senior Data Engineer

We are seeking a hands-on Senior Data Engineer to lead the development, support, and continuous improvement of our Azure-based data platform. This role combines data engineering, platform operations, and technical leadership, ensuring reliable, scalable, and high-performing data solutions across the enterprise.

Key Responsibilities

• Design, build, and support scalable data pipelines using Azure Databricks, PySpark, Python, and SQL.
• Develop and maintain data ingestion, transformation, and orchestration solutions across the Azure data ecosystem.
• Provide production support, incident resolution, root cause analysis, and platform monitoring.
• Implement integrations with Braze and other downstream data consumers.
• Drive platform reliability through automation, observability, CI/CD, and operational best practices.
• Partner with architects, product teams, and stakeholders to deliver data platform enhancements.
• Lead technical delivery, code reviews, and engineering standards while coordinating offshore development teams.
• Contribute to cloud infrastructure, Infrastructure-as-Code, and platform optimization initiatives.

Required Skills & Experience

• 6+ years of experience in Data Engineering and Data Platform delivery.
• Strong expertise in:
• Python
• SQL
• Azure DevOps and CI/CD
• Experience supporting enterprise-scale data platforms in production environments.
• Strong troubleshooting, performance tuning, and operational support skills.
• Experience leading technical delivery and collaborating with offshore teams.
• Excellent communication and stakeholder management skills.

Preferred Qualifications

• Terraform or Bicep (Infrastructure as Code).
• Azure platform engineering and cloud infrastructure experience.
• Monitoring and observability tools (Azure Monitor, Log Analytics, Splunk, Dynatrace, etc.).
• Production release management and platform operations experience.
• Experience with Braze, Martech, or Customer Data Platforms (CDP).

Nice to Have

• Event-driven and streaming architectures.
• Data quality, governance, and observability frameworks.