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.

Bachelor’s degree in computer science or related field
4 years of experience in data engineering, data platform development, or cloud data solutions
2 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
Expertise with Databricks, specifically the ability to design enterprise-level strategy and architecture including Unity Catalog, data warehousing, data sharing
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

Data Engineer

Key skills