Work directly with clients to understand business goals, data challenges, and technical requirements
Lead or support discovery sessions, requirements workshops, architecture discussions, and solution reviews
Develop and optimize batch and streaming data ingestion pipelines from enterprise applications, databases, APIs, and file-based sources
Implement medallion/Lakehouse architectures, dimensional models, and data transformation workflows to support analytics and reporting use cases
Engineer solutions using technologies such as PySpark, Spark SQL, SQL, Python, Delta Lake, and orchestration tools within Azure and Databricks
Recommend best practices for data modeling, governance, lineage, monitoring, DevOps, and security
Help establish engineering standards, reusable frameworks, accelerators, and documentation for internal and client teams
Contribute to estimation, project planning, and delivery management for client engagements

Bachelor’s degree in computer science or related field
8 years of experience in data engineering, data platform development, or cloud data solutions
3 years of hands-on experience with Azure Databricks, Apache Spark, or similar distributed data processing technologies
Expertise with Microsoft Azure infrastructure and data resources, including Fabric, Azure Data Factory, Synapse Data Analytics, Power BI, Azure SQL, Azure Cosmos DB
Deep expertise with Databricks, specifically the ability to design enterprise-level strategy and architecture including Unity Catalog, data warehousing, data sharing, and Mosaic AI
Experience setting end-to-end modern data platforms in Azure including architecture/design, ingestion, storage strategies, analytics & reporting, Apache Spark, and networking requirements
DevOps for data, GitHub, automated testing, and working with containers (AKS, Docker, registries, etc.)
Excellent communication skills, ability to clearly explain concepts to teammates and customers, and quickly learn new concepts and technologies.

Senior Data Engineer

Key skills