Data Engineering & Analytics Lead (CST timing)

We are looking for a strong Data Engineering & Analytics professional with 10+ years of experience in building data pipelines, data models and analytics solutions, with hands-on expertise in Databricks, SQL, PySpark, Python and Power BI.

Work Mode: Remote

Work Time: 6.30 PM to 3:00 AM (CST Time zone)

Primary Skills :

• Databricks (Delta Lake, Lakehouse, data transformation, performance optimization)
• SQL (advanced queries, joins, window functions, performance tuning, data transformation)
• PySpark (transformations, joins, aggregations, window functions, deduplication, optimization )
• Python
• ETL / ELT Pipeline Development
• Data Modeling & Schema Design
• Power BI (data modelling, dashboards, analytics)
• Azure Data Factory (ADF)
• Data Lake / Lakehouse Architecture / Medallion Architecture
• AI-assisted Data Analysis & Analytics (leveraging AI/LLMs for data analysis, insight generation and analytics development)

Responsibilities include:

• Design, develop, and maintain scalable ETL/ELT pipelines using Databricks, PySpark, Python, and Azure Data Factory.
• Build and optimize enterprise-grade data solutions using Databricks Lakehouse architecture and Delta Lake.
• Develop high-performance data transformation workflows and implement data quality controls.
• Create and maintain logical and physical data models to support reporting, analytics, and business intelligence requirements.
• Write and optimize complex SQL queries, stored procedures, views, and data transformation logic.
• Design and implement Medallion Architecture (Bronze, Silver, Gold layers) for modern analytics platforms.
• Develop interactive dashboards, reports, and analytical solutions using Power BI.
• Collaborate with business stakeholders to understand reporting and analytics requirements.
• Ensure data governance, metadata management, and data cataloging best practices are followed.
• Implement performance optimization strategies across data pipelines, Databricks workloads, and reporting solutions.
• Leverage AI/LLM-enabled tools for data analysis, insight generation, and analytics acceleration.
• Work closely with cross-functional teams including Data Architects, Business Analysts, and Product Owners.
• Support CI/CD and version control practices for data engineering solutions.