Role: AWS Redshift Lead ( Contract for 12 to 18 months)
Exp: 8-10 Yrs
Work Mode: 5 days WFO
Interview: Virtual
Location: PAN INDIA
Key Responsibilities:
· Design and manage scalable Redshift data warehouses for analytical workloads.
· Implement and maintain Star Schema-based dimensional models to support data marts and business reporting.
· Develop complex SQL queries and scripts for data transformation, aggregation, and cleansing.
· Optimize Redshift clusters, queries, and data distribution strategies for performance and cost.
· Integrate data from multiple sources (including S3, Postgres, APIs, and third-party feeds) into Redshift using ETL/ELT tools.
· Collaborate with Data Architects and Modelers to ensure data models are efficient, scalable, and align with business requirements.
· Ensure data integrity, availability, security, and governance across the Redshift environment.
· Monitor system performance, perform tuning, and troubleshoot operational issues.
· Participate in Agile/Scrum ceremonies and work collaboratively with cross-functional teams.
Required Qualifications:
· 5+ years of experience in data engineering or warehousing with a focus on Amazon Redshift.
· Strong proficiency in SQL, with ability to write and optimize complex queries for large datasets.
· Solid understanding of dimensional modeling, Star Schema, and OLAP vs OLTP data structures.
· Experience in designing analytical data marts and transforming raw/transactional data into structured analytical formats.
· Hands-on experience with ETL tools (e.g., AWS Glue).
· Familiarity with Amazon Redshift Spectrum, RA3 nodes, and data distribution/sort keys best practices.
· Comfortable working in cloud-native environments, particularly AWS (S3, Lambda, CloudWatch, IAM, etc.).
Preferred Qualifications:
· Exposure to data lake integration, external tables, and Redshift-Unload/Copy operations.
· Experience in BI tools (e.g., Tableau, QuickSight) to validate and test data integration.
· Familiarity with Python or PySpark for data transformation scripting.
· Understanding of CI/CD for data pipelines and version control using Git.
· Knowledge of data security, encryption, and compliance in a cloud environment.
