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We are seeking a skilled and motivated Data Engineer to join our team, with deep hands-on experience building and optimizing data pipelines and lakehouse solutions in Databricks. In this role, you will collaborate with cross-functional teams to design, develop, and operate scalable, reliable data products that drive business value.
Job Responsibility:
Design, build, and maintain batch and streaming data pipelines using Databricks (Spark, Delta Lake, Jobs/Workflows)
Partner with data scientists, analysts, and application teams to deliver trusted, well-modeled data sets and features in the Databricks Lakehouse
Optimize Spark jobs (partitioning, caching, join strategies) and Databricks cluster configurations for performance, scalability, and cost
Implement data quality checks, observability, governance, and security controls (e.g., Unity Catalog, access policies) within Databricks
Troubleshoot and resolve pipeline failures, data issues, and production incidents
perform root-cause analysis and implement preventative improvements
Requirements:
Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience)
Proven experience delivering production data engineering solutions in Databricks, including Apache Spark and Delta Lake on the Microsoft Azure cloud platform
Strong proficiency in SQL and Python (or Scala), including building ETL/ELT pipelines and writing efficient Spark code
Experience with data modeling and lakehouse/warehouse patterns (e.g., medallion architecture) and distributed data processing concepts
Excellent problem-solving skills and attention to detail
Strong communication and teamwork abilities
Nice to have:
Hands-on experience with Databricks ecosystem capabilities such as Unity Catalog, Delta Live Tables, MLflow, and/or Databricks SQL
Experience implementing CI/CD and automated testing for Databricks notebooks/repos and deployment of Jobs/Workflows
Knowledge of data security, privacy, and compliance controls in cloud lakehouse environments (including role-based access and auditing)