Databricks Engineer with PySpark and Python

Databricks Engineer Pyspark–

Santa Clara, CA-- 5 days onsite - -(Day1 Onsite) – Locals Only

Azure Databricks Tech Lead

Rate: 60/hr C2C

• Lead the end to end migration of data pipelines, workflows, and integration patterns from Informatica to Azure Databricks.
• Assess current state Informatica assets and define target state architecture, migration strategy, and phased execution plans. The current environment is based on Informatica PowerCenter 10.4.1 running batch ETL workloads, with Oracle as both the source and target database. The target state platform is Azure Databricks (Runtime 14.4 LTS) with Unity Catalog, Delta Live Tables (DLT), and ADLS Gen2.
• Drive modernization of ETL patterns into scalable ELT and data engineering approaches aligned with Azure Databricks best practices.
• Analyze and interpret complex Informatica PowerCenter mappings, including transformations, lookups, joins, filters, routers, and expression logic, and build equivalent PySpark based pipelines in Azure Databricks.
• Assess Oracle database level stored procedures, functions, and packages that are invoked by or tightly coupled with Informatica workflows, and determine whether to migrate, refactor, or recreate that logic within Databricks using PySpark, Python, or Spark SQL.
• Provide technical leadership across solution design, engineering standards, code quality, testing strategy, deployment, and production readiness.
• Partner with business, product, architecture, infrastructure, security, and operations stakeholders to align migration outcomes with enterprise priorities.
• Establish migration success criteria covering data quality, reconciliation, performance, resilience, observability, and business continuity.
• Identify delivery risks, dependencies, and technical constraints early, and drive mitigation plans through execution.
• Guide cutover, stabilization, and post migration optimization to ensure sustainable platform adoption and operational excellence.
• Mentor engineers and technical teams on Databricks design patterns, reusable frameworks, and modern data engineering practices