Required Skills:
- AWS Data Services
-
Data Pipelines / ETL
-
Python
-
SQL / Data Warehousing
-
Big Data
-
CI/CD
-
Git / Linux
Nice to Have:
- Data Testing
-
AWS Glue / Catalog
Experience range: 6 to 8 overall years of exp should be OK. Job Title: Data Engineer – Data Platform Engineer – GOLD Layer & Data Distribution Role Overview We are seeking a Data Engineer with strong expertise in Data Warehousing, ETL development, SQL, and Informatica technologies to support and enhance both our existing legacy data landscape and modern AWS-based data platform. The role will be responsible for maintaining and evolving enterprise data pipelines, supporting warehouse modernization initiatives, building business-ready data layers (GOLD), and enabling secure, governed data distribution and discovery for analytics, reporting, and AI use cases. The successful candidate should be comfortable working across traditional data warehouse environments as well as cloud-native AWS technologies. ________________________________________ Key Responsibilities 1. Data Warehouse & Legacy Platform Support • Support, maintain, and enhance existing enterprise data warehouse solutions and associated ETL processes. • Develop, troubleshoot, and optimize ETL workflows using Informatica PowerCenter and/or Informatica Cloud (IICS). • Perform data analysis, data validation, and root cause analysis for production issues. • Design and implement efficient database solutions and optimize SQL queries for performance and scalability. • Work with business and technical stakeholders to understand data integration requirements and translate them into robust ETL solutions. • Ensure data quality, consistency, and integrity across source systems and downstream consumers. • Support ongoing migration and modernization initiatives from legacy platforms to cloud-based solutions. Mandatory Skills • Strong SQL expertise (complex joins, window functions, aggregations, query tuning) • Database concepts and administration knowledge • ETL design, development, and support • Informatica PowerCenter and/or Informatica Cloud (IICS) • Data Warehousing concepts • Dimensional Modelling (Kimball methodology) • Star Schema and Fact/Dimension modelling • Data Integration and Data Quality principles ________________________________________ 2. Build and Enhance GOLD Data Layer • Design and implement business-ready, curated GOLD datasets for reporting, analytics, AI, and self-service consumption. • Build and maintain ETL/ELT pipelines using cloud-native technologies where applicable. • Apply dimensional modelling techniques and create highly optimized analytical data models. • Develop datasets tailored for KPIs, dashboards, advanced analytics, and GenAI use cases. • Optimize data structures and workloads for performance and cost efficiency. • Implement metadata and semantic layer capabilities to improve data discoverability and usability. Technologies & Skills • SQL and Data Modelling • Amazon Redshift • AWS Glue (ETL processing) • Python/PySpark • Amazon Athena • Parquet and Columnar Storage Optimization ________________________________________ 3. Ensure Secure and Governed Data Distribution & Discovery • Implement and support data governance practices across the enterprise data platform. • Manage metadata, data classification, and data catalog capabilities. • Support secure data access using role-based and least-privilege access principles. • Enable trusted data discovery and self-service analytics. • Support compliance, auditing, and data security requirements. • Work with business stakeholders to define data ownership, stewardship, and governance standards. Technologies & Skills • AWS Lake Formation • AWS Glue Data Catalog • AWS IAM • Data Governance and Metadata Management • Data Security and Compliance Standards (GDPR, etc.) ________________________________________ Mandatory Skills • SQL • Database (Amazon Redshift preferred) • ETL Development • Informatica PowerCenter and/or Informatica Cloud (IICS) • Data Warehousing Concepts • Dimensional Data Modelling • Data Analysis and Troubleshooting • Strong understanding of Data Integration Architecture • S3 and other AWS Data Services ________________________________________ Good to Have Skills • AWS Glue • Python / PySpark • Amazon Athena • AWS Lake Formation • AWS IAM • AWS Data Catalog • Airflow • GenAI/Data Analytics Enablement ________________________________________ Preferred Qualifications • 5+ years of experience in Data Engineering, ETL Development, or Data Warehousing. • Hands-on expertise with Informatica PowerCenter and/or Informatica Cloud. • Strong understanding of enterprise data warehouse architecture and dimensional modelling. • Experience supporting both legacy data platforms and modern cloud-based data ecosystems. • Exposure to AWS data services and cloud migration initiatives. • Familiarity with BI, analytics, and AI/ML data consumption patterns. • Excellent problem-solving, stakeholder management, and communication skills. Mandatory Skills 1. Data Engineering (min 5 years) 2. AWS (Redshift, S3, Lambda, EMR, etc.) 3. Python 4. SQL 5. Spark / Hive / Presto 6. Data Warehousing & Data Modeling Preferred Skills 1. DevSecOps / CI-CD 2. Git & Linux 3. Redshift expertise 4. Data Lake architecture 5. Performance optimization experience 6. Consulting/stakeholder-facing experience Nice to Have 1. Apache Spark (strong preference) 2. AWS Glue 3. Data Lake implementations 4. Agile / DevOps experience
