Azure Data Engineer

Technical Leadership:

Lead the design and implementation of enterprise-grade Azure data solutions (e.g., Data Factory, Synapse, Databricks, Data Lake).
Define architectural standards, best practices, and governance for data pipelines, ETL/ELT processes, and warehousing. Mentor and manage a team of data engineers, fostering skill development and ensuring high-quality deliverables. Collaborate with stakeholders to translate business needs into technical roadmaps.

Data Engineering & Development:

Build robust, scalable data pipelines for ingesting, transforming, and storing structured/unstructured data.
Optimize SQL queries, stored procedures, and database performance for large-scale datasets. Implement data warehousing solutions (e.g., star/snowflake schemas), data vault modelling and ensure alignment with business requirements. Leverage Power BI to enable self-service analytics by integrating curated datasets into dashboards (good-to-have).

Cloud & DevOps Integration:

Automate deployments using Azure DevOps (CI/CD pipelines, ARM templates) or GitHub actions and ensure IaC (Infrastructure as Code) practices.
Monitor and troubleshoot Azure resources for cost, performance, and security compliance. Stay updated on emerging Azure services (e.g., Purview, Fabric) and drive innovation.

Qualifications:

Minimum 5 years of hands-on experience in Azure data integration tools like Data Factory, Synapse, Data Lake and Blob Storage. Expert-level SQL skills and experience with relational databases (e.g., SQL Server, Oracle and PostgreSQL). Strong knowledge of data warehousing, ETL/ELT design, and data modeling. Proven track record of leading teams or managing complex projects end-to-end. Familiarity with Power BI (DAX, data modeling) for analytics enablement is a plus. Proficiency in Python or PySpark for data processing/automation. Certifications like Azure Data Engineer Associate (DP-203), Fabric (DP-700) or Azure Solutions Architect preferred.