Primary (Must have skills)* - To be Screened by TA Team | 1). 10+ years of overall IT experience, with 8+ years in Data Engineering & Analytics, including end-to-end data solution design, architecture, and enterprise data platform implementation
2). 6+ years of hands-on experience in AWS data architecture, including Amazon S3, AWS Glue, Redshift, Athena, Lambda, EMR, Lake Formation, Kinesis, and RDS with strong understanding of cloud-native data patterns & SQL development and advanced data modeling
3). 3+ years of experience in integrating Salesforce (Sales Cloud / Service Cloud) with external data platforms using APIs, connectors, and middleware tools
4). 3+ years of experience in leading data engineering teams as Technical Architect / Lead, providing architecture guidance, code reviews, design standards, and mentoring developers
5) 4+ years of hands-on experience with Tableau, including dashboard development, data modeling, performance tuning, and data visualization best practices
6). 3+ years of experience in real-time and batch data processing architectures, including streaming frameworks and large-scale data and ingestion and experience in CI/CD |
Job Description of Role* (RNR) - To be Evaluated by Technical Panel (Define it to give more clarity) | Design and define enterprise-level data architecture across AWS data platforms, ensuring scalability, security, performance, and alignment with business strategy.
Architect, develop, and oversee implementation of data lakes/lakehouse solutions using AWS services such as S3, Glue, Redshift, Athena, EMR, Lambda, and Lake Formation.
Lead the design and development of robust ETL/ELT pipelines (batch and real-time), ensuring high data quality, reliability, and performance optimization.
Integrate and synchronize data between Salesforce and AWS data platforms
Define and implement enterprise data models (conceptual, logical, physical), including dimensional modeling (Star/Snowflake schemas) to support analytics and reporting needs.
Establish BI architecture standards using Tableau, including semantic layer design, dashboard governance, security (row-level security), and performance optimization.
Translate complex business requirements into scalable technical architecture and actionable data solutions.
Provide hands-on technical leadership in SQL, Python/PySpark, query optimization, and performance tuning for large-scale datasets.
Define and enforce data governance, security frameworks (IAM, encryption, masking), and compliance standards across the data ecosystem.
Guide teams in implementing CI/CD pipelines, Infrastructure as Code (Terraform/CloudFormation), and DevOps best practices for data platforms.
Review solution designs and code to ensure adherence to architectural standards, scalability principles, and best practices.
Collaborate with cross-functional teams to gather requirements and deliver scalable solutions
Drive modernization initiatives including migration from legacy/on-prem systems to AWS cloud environments.
Ensure monitoring, logging, observability, backup, and disaster recovery strategies are implemented effectively.
Mentor and technically guide data engineers and BI developers, establishing development standards and reusable frameworks.
Continuously evaluate emerging technologies, BI trends, and AWS innovations to recommend improvements and optimize enterprise data capabilities. |
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