Role Overview
We are looking for a highly experienced Lead Data Engineer with 10+ years of experience in building scalable data platforms and modern data solutions. The ideal candidate will have strong expertise in Azure, Databricks Lakehouse architecture, and big data technologies, along with proven experience in leading engineering teams and delivering enterprise-grade data pipelines.
Key Responsibilities
- Design, develop, and maintain scalable data pipelines using PySpark and Databricks
- Architect and implement modern data platforms using Azure (ADF, ADLS, Databricks)
- Lead end-to-end data engineering projects including ingestion, transformation, and data delivery
- Optimize data processing performance using Spark, Delta Lake (partitioning, Z-ordering)
- Implement data governance, security, and access controls (Unity Catalog)
- Migrate legacy ETL/PLSQL workloads to modern cloud-based architectures
- Build and manage orchestration workflows using Databricks Workflows / Control-M
- Integrate data platforms with Snowflake and other analytics systems
- Perform code reviews, mentor team members, and ensure best practices
- Collaborate with stakeholders in Agile environments
Required Skills & Experience
- 10+ years of experience in Data Engineering / Data Warehousing
- Strong hands-on experience in Python, PySpark, SQL, and PL/SQL
- Expertise in Apache Spark and Databricks (Lakehouse, Delta Lake)
- Solid experience with Microsoft Azure (ADF, ADLS, Azure Databricks)
- Experience with Snowflake or similar cloud data warehouses
- Strong knowledge of ETL frameworks and orchestration tools
- Experience with CI/CD tools like Azure DevOps or Jenkins
- Good understanding of data modeling and performance tuning
Preferred Qualifications
- Databricks Certifications (Associate / Professional)
- Experience in Banking, Healthcare, or Telecom domains
- Exposure to Unix shell scripting and job scheduling tools
- Strong leadership and stakeholder management skills
