Key Skills & Qualifications :
- 5+ years of experience in Data Warehousing (DWH) and ETL development, preferably within AWS and Databricks environments.
- At least 2 years of experience building robust data pipelines using Databricks, PySpark, and SQL.
- Strong expertise in SQL writing, optimizing queries, and understanding data modeling and governance best practices.
- Hands-on experience with SQL Server, Oracle, and / or cloud databases.
- In-depth knowledge of data warehousing concepts, including Star and Snowflake schemas.
- Experience in data ingestion and transformation from files, databases, and other sources.
- Proven ability to conduct data profiling and extract meaningful design insights.
- Strong understanding of enterprise business processes and system interdependencies.
- Excellent communication skills with the ability to engage directly with end-users.
- Capable of managing multiple clients or projects simultaneously.
- Strong analytical skills and attention to detail with a focus on documentation and process clarity.
Roles & Responsibilities :
Engage with business stakeholders to gather and analyze reporting and analytics requirements, and develop data solutions accordingly.Collaborate with application developers and business analysts to implement and fine-tune AWS and Databricks-based data workflows.Design, develop, and maintain data pipelines using Databricks (Delta Lake, Spark SQL, PySpark), AWS Glue, and Apache Airflow.Build and orchestrate ETL processes using Databricks notebooks, PySpark, and AWS Glue, ensuring efficient and reliable data transformation.Write and optimize SQL scripts, queries, views, and stored procedures to support performance-focused data models.Investigate and resolve production issues through root cause analysis and provide timely resolutions for data inconsistencies.Maintain up-to-date technical documentation, including data models, data flows, and field-level mappings.Support daily batch processing and ensure consistent, reliable data delivery to downstream systems.Continuously refine database schemas, Delta Lake structures (Delta Tables, Parquet), and views to maintain high data integrity and system performance.ref : hirist.tech)