Design, develop, and maintain robust enterprise data warehouse solutions that support data science, artificial intelligence, and business intelligence requirements.
Architect scalable ETL/ELT pipelines to efficiently transform raw data into structured, analytics-ready formats.
Build and manage API integrations, including hands-on API development.
Utilize T-SQL and Azure Data Factory to create, optimize, and manage data integration workflows.
Use Microsoft Fabric notebooks for transformation and orchestration where appropriate, leveraging Lakehouse and Warehouse for supplemental storage.
Ensure high data quality, integrity, and performance through meticulous query tuning and process optimization.
Collaborate with data scientists, software developers, business intelligence teams, and stakeholders to develop and deploy data solutions that meet business needs.
Translate business requirements into technical solutions and coordinate smoothly between engineering and other teams.
Lead the creation of scalable, reliable data models and optimize them for performance and usability.
Drive continuous improvement in data engineering processes and practices to keep them efficient and aligned with industry best practices.
Monitor system performance and proactively implement improvements to maximize efficiency and scalability.
Troubleshoot and resolve data-related issues to ensure reliable data delivery.

5+ years of hands-on experience in data warehousing, data engineering, or a similar role.
Extensive experience with T-SQL, including advanced query development and performance tuning.
5+ years as a SQL Server / Azure SQL DBA — performance tuning, index and statistics management, execution-plan analysis, and proactive capacity planning.
Proficiency with pipeline development and configuration using Azure Data Factory (ADF).
Working familiarity with Microsoft Fabric (notebooks and pipelines for transformation, Lakehouse, and Warehouse)
Expertise in Python for data engineering tasks, including data manipulation and workflow management.
Strong understanding of data modeling, data architecture, and best practices in data governance.
Experience handling sensitive/PII data and supporting data quality and governance in a regulated, financial-services environment.
Experience preparing clean, analytics
and ML-ready datasets to support data science and AI workloads.
Excellent problem-solving skills and the ability to work independently as well as collaboratively.
Strong communication skills to effectively liaise with both technical teams and non-technical stakeholders.

Senior Data Engineer

Key skills