Required Skills:
- ETL Development
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SQL
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AWS Data Services
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Relational Databases
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Data Modeling
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Production Support
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Python
Nice to Have:
- Informatica
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Apache Spark
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DBT
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Apache Airflow
Role Overview We are seeking a highly motivated and self-driven Data Engineer with strong expertise in ETL Development, Data Warehousing, SQL, Database Technologies, and AWS-based Data Platforms. This is a hands-on individual contributor role requiring end-to-end ownership of data engineering deliverables, including design, development, optimization, production support, and issue resolution across enterprise data platforms. The ideal candidate should possess excellent SQL skills, extensive experience with relational databases, and a strong understanding of enterprise data warehousing concepts. Experience with AWS data services is essential, while exposure to Informatica, Redshift, Python, Spark, DBT, and AWS Glue will be an added advantage. Key Responsibilities 1. Data Engineering & Data Warehouse Development Design, develop, maintain, and support enterprise-scale ETL/ELT pipelines. Build and optimize enterprise Data Warehouse solutions. Develop and optimize SQL scripts for data extraction, transformation, integration, and reporting. Analyze and tune SQL queries for high-performance processing of large datasets. Design scalable data models and data integration solutions. Investigate production issues, perform root cause analysis, and resolve data-related incidents independently. Ensure data quality, consistency, integrity, and accuracy across multiple source systems. Support cloud migration, modernization, and continuous platform improvements. 2. Enterprise Data Layer Development Design and implement curated, business-ready datasets for reporting, analytics, dashboards, and AI use cases. Build scalable data pipelines using traditional ETL and cloud-native technologies. Apply dimensional modeling techniques (Star Schema, Snowflake Schema) to support analytical workloads. Design reusable, maintainable, and scalable data engineering solutions. Continuously improve platform performance, scalability, reliability, and operational stability. 3. AWS Data Platform & Cloud Enablement Develop and support AWS-based data ingestion, transformation, and consumption pipelines. Work with AWS cloud data services to enable secure, governed, and scalable data access. Support metadata management, data cataloging, and governance best practices. Collaborate with business users, architects, and technical teams to understand requirements and deliver high-quality solutions. Own assigned deliverables from requirement gathering through production deployment and ongoing support. Mandatory Skills ETL & Data Warehousing Strong experience in ETL design, development, implementation, and support. Strong understanding of Enterprise Data Warehousing concepts. Experience with dimensional modeling and data integration architecture. Experience designing and maintaining enterprise-scale data pipelines. Hands-on experience processing and transforming large-volume datasets. SQL & Database Technologies Strong expertise writing complex SQL queries in production environments. Experience optimizing SQL performance and query tuning. Strong knowledge of: Complex Joins Subqueries Common Table Expressions (CTEs) Window Functions Aggregations Views Stored Procedures Ability to analyze execution plans and optimize database performance. Hands-on experience with one or more relational databases such as: Oracle SQL Server PostgreSQL MySQL Teradata DB2 AWS Cloud & Data Platform Exposure to AWS cloud services and cloud-native data engineering platforms. Understanding of cloud-based data architecture and modern data engineering concepts. Experience working with AWS Data & Analytics services is preferred. Additional Required Skills Data Analysis & Troubleshooting Data Quality Validation Root Cause Analysis Production Support Performance Optimization Stakeholder Collaboration End-to-End Ownership of Deliverables Strong Analytical & Problem-Solving Skills Excellent Communication Skills Good-to-Have Skills Informatica PowerCenter Informatica Intelligent Cloud Services (IICS) Amazon Redshift AWS Glue Python PySpark Apache Spark DBT (Data Build Tool) Amazon Athena Amazon S3 Apache Airflow AWS Data & Analytics Services Preferred Qualifications 5+ years of experience in Data Engineering, ETL Development, or Data Warehousing. Strong hands-on experience developing and optimizing complex SQL queries in enterprise environments. Proven experience handling large-scale datasets and performance tuning. Strong understanding of Enterprise Data Warehouse architecture and dimensional modeling. Experience working with AWS-based data platforms and cloud-native data services. Exposure to Informatica, Redshift, Python, Spark, AWS Glue, Airflow, or DBT is highly desirable. Demonstrated ability to independently own deliverables from requirements gathering through production support. Experience working directly with business stakeholders and cross-functional teams. Excellent analytical, troubleshooting, communication, and problem-solving skills. Experience 5+ Years in Data Engineering, ETL Development, or Data Warehousing. Education Bachelor's Degree in Computer Science, Information Technology,
