
Jellyfish Technologies
1 week ago
Position Summary :
The ideal candidate will have extensive experience with Apache Spark, Delta Lake, PySpark/Scala, and cloud platforms (Azure, AWS, or GCP), along with a proven ability to define best practices for architecture, governance, security, and performance optimization on Responsibilities :
- Define scalable architecture for data ingestion, ETL/ELT pipelines, data processing, analytics, and data science workflows.
- Develop reference architectures and solution blueprints for various business and technical use cases.
- Lead the development of robust data pipelines and ETL frameworks using PySpark/Scala and Databricks notebooks.
- Enable streaming and batch data processing using Apache Spark on Databricks.
- Collaborate with DevOps teams to implement CI/CD pipelines for Databricks workloads using tools like GitHub, Azure DevOps, or Jenkins.
- Optimize Databricks clusters, Spark jobs, and data workflows for performance, scalability, and cost efficiency.
- Implement caching, partitioning, Z-Ordering, and data compaction strategies on Delta Lake
- Define and implement data governance standards using Unity Catalog, role-based access control (RBAC), and data lineage tracking.
- Ensure data compliance and security policies are enforced across data pipelines and storage layers.
- Maintain metadata catalogs and ensure data quality and observability across the pipeline.
- Engage with business analysts, data scientists, product owners, and solution architects to gather requirements and translate them into technical solutions.
- Present architectural solutions and recommendations to senior leadership and cross-functional teams.
- Provide technical guidance and mentorship to data engineers and junior architects.
- Conduct code reviews, enforce coding standards, and foster a culture of engineering Qualifications Skills :
- Expert-level knowledge of Databricks, including Delta Lake, Unity Catalog, MLflow, and Workflows.
- Strong hands-on experience with Apache Spark, especially using PySpark or Scala.
- Proficient in building and maintaining ETL/ELT pipelines in a large-scale distributed environment.
- In-depth understanding of cloud platforms AWS (with S3, Glue, EMR), Azure (with ADLS, Synapse), or GCP (with BigQuery, Dataflow).
- Familiarity with SQL and data modeling techniques for both OLAP and OLTP systems
(ref:hirist.tech)
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