Embrace what’s next.
Create new possibilities.

Job Purpose and Impact

• • The Senior Data Engineer will be a core contributor to the architecture, delivery, and operations of the Company's internal business intelligence and data platform, which delivers automated ETL, ad-hoc and automated analytics, and embedded dashboards to users across the enterprise. The successful candidate will own day-to-day data engineering architecture, accelerate feature delivery and performance tuning, and mentor the team's data professionals to ensure the platform scales sustainably with enterprise demand. 

Key Accountabilities

• • Owns and evolves the architectural design of the data platform - Azure Data Factory, Azure Blob Storage, Azure SQL Server, and on-premises ETL/data-acquisition components - maintaining architecture standards, decision records, and consistency of data feeds, models, and pipeline design. 
  • Analyzes and optimizes query performance, including indexing strategy, execution-plan analysis, and tuning of slow workloads; leads break-fix for performance degradation. 
  • Designs, builds, and maintains ETL/ELT pipelines spanning on-premises source systems to Azure, with an emphasis on reliability, idempotency, and data quality. 
  • Manages database lifecycle operations - schema changes, migrations, and maintenance with a zero-downtime discipline - and delivers feature work to clear and prevent delivery backlogs. 
  • Owns CI/CD for database and pipeline deployments, including version control, peer review, and audited rollouts and rollbacks; monitors platform health, pipeline success rates, and resource/cost consumption, driving capacity and cost optimization. 
  • Designs and builds Apache Superset dashboards, balancing dashboard authoring with architectural priorities; dashboards are auto-mapped and embedded for customers via the platform's tagging system, eliminating manual publishing work. 
  • Monitors the platform on behalf of customers, including automated and AI-driven features, to ensure capabilities behave reliably; contributes to automation and self-healing service scaling to minimize manual intervention, shifting toward quality, governance, and monitoring of AI-generated outputs as generative-AI dashboard creation matures. 
  • Contributes to integrating AI capabilities into the platform - including Model Context Protocol (MCP), Superset MCP, and Azure AI Foundry readiness - and evaluates emerging data and AI technologies for adoption. 
  • Collaboration, Mentoring & Security practices

Qualifications

• • 5-6 years experience experience designing, building, and supporting enterprise data platforms.
  • Bachelor’s degree in Computer Science, Data Engineering, Information Technology, or a related field, or equivalent practical experience.
  • Significant professional experience designing, building, and supporting enterprise data platforms.
  • Strong hands-on experience with Azure Data Factory, Azure Blob Storage, and Azure SQL Server.
  • Advanced proficiency in T-SQL, including query optimization, execution-plan analysis, indexing strategies, stored procedures, tables, and views.
  • Demonstrated experience building reliable ETL/ELT pipelines that integrate on-premises systems with Azure cloud environments.
  • Experience managing database schema changes, migrations, maintenance, and production performance issues.
  • Experience implementing CI/CD practices for database and data-pipeline deployments, including version control, peer review, deployment, and rollback processes.
  • Strong understanding of data modeling, data quality, platform monitoring, access controls, audit logging, and secure deployment practices.
  • Ability to independently make technical and architectural decisions in a complex enterprise environment.
  • Strong communication and stakeholder-management skills, with the ability to collaborate across global teams and mentor other data professionals.