Description

You will own the design and implementation of data platforms for financial data workloads.

Responsibilities

• Design and implement scalable ETL/ELT pipelines using PySpark, Python, and Databricks for large-volume financial data.
• Define and enforce data quality standards, governance, and security across all data assets.
• Write clean, efficient code in Python and PySpark to transform raw data into curated datasets.
• Mentor data engineers, guide technical execution, and conduct thorough code reviews.
• Drive the technical architecture of the modern data platform on Databricks, including Delta Lake optimization.

Required Skills

• 8+ years of progressive experience in Data Engineering, including senior or lead capacity.
• Exceptional proficiency in Python (including Pandas, NumPy) and PySpark.
• Extensive, hands-on experience with the Databricks platform (Workspace, Clusters, Jobs, Delta Lake, Unity Catalog).
• Strong understanding of data warehousing concepts and SQL/NoSQL databases.
• Experience with major cloud providers (AWS, Azure) and their data services.
• Proficiency with Infrastructure as Code tools like Terraform.
• Demonstrated ability to lead technical projects and manage development cycles.

Key Skills

Education

Any Graduate