Design, implement, and optimize big data pipelines in Databricks.

Develop scalable ETL workflows to process large datasets.

Leverage Apache Spark for distributed data processing and real-time analytics.

Implement data governance, security policies, and compliance standards.

Optimize data lakehouse architectures for performance and cost-efficiency.

Collaborate with data scientists, analysts, and engineers to enable advanced AI/ML workflows.

Monitor and troubleshoot Databricks clusters, jobs, and performance bottlenecks.

Automate workflows using CI/CD pipelines and infrastructure-as-code practices.

Ensure data integrity, quality, and reliability in all pipelines.

Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.

5+ years of hands-on experience with Databricks and Apache Spark.

Proficiency in SQL, Python, or Scala for data processing and analysis.

Experience with cloud platforms (AWS, Azure, or GCP) for data engineering.

Strong knowledge of ETL frameworks, data lakes, and Delta Lake architecture.

Experience with CI/CD tools and DevOps best practices.

Familiarity with data security, compliance, and governance best practices.

Strong problem-solving and analytical skills with an ability to work in a fast-paced environment.

Databricks certifications (e.g., Databricks Certified Data Engineer, Spark Developer).

Hands-on experience with MLflow, Feature Store, or Databricks SQL.

Exposure to Kubernetes, Docker, and Terraform.

Experience with streaming data architectures (Kafka, Kinesis, etc.).

Strong understanding of business intelligence and reporting tools (Power BI, Tableau, Looker).

Prior experience working with retail, e-commerce, or ad-tech data platforms.