5+ years of experience in designing and implementing data warehouses and data lakes/lakehouses on AWS.
Hands-on experience with AtScale or similar semantic layer tools to enable governed, business-friendly data access across BI platforms.
Proven success working with globally distributed teams in collaborative delivery environments.
Deep working knowledge across key AWS Data & Analytics services, including: Building large-scale data lake architectures on Amazon S3 and open table formats.
Implementing governance and cataloging through AWS Lake Formation.
Developing ETL and metadata frameworks using AWS Glue.
Leveraging AWS Lambda for serverless data processing.
Running distributed data workloads on Amazon EMR.
Enabling real-time data pipelines with AWS Kinesis (Data Streams and Firehose).
Orchestrating pipelines using AWS Step Functions/Amazon MWAA/similar services.
Designing and optimizing schemas and query performance on Amazon Redshift, including Spectrum and Serverless features.
Querying large datasets interactively using Amazon Athena.
Managing operational databases using Amazon RDS across engines such as PostgreSQL, MySQL, and Aurora.
Integrating and migrating data using AWS DMS, Glue Connectors, EventBridge, SNS, and SQS.
Strong understanding of semantic modeling, including logical data models, virtual cubes, and centralized metric definitions (single source of truth).
Experience optimizing performance using query pushdown, caching, and aggregate awareness over platforms like Redshift and Athena.
Ability to integrate semantic layers with BI tools (QuickSight, Tableau, Power BI) and enforce row/column-level security aligned with governance frameworks.
Strong programming capability in Python and PySpark for large-scale data processing.
Proficiency in writing complex SQL queries, analytical functions, and performance tuning for large datasets.
Familiarity with NoSQL databases such as Amazon DynamoDB, MongoDB, or DocumentDB.
Strong understanding of partitioning, indexing, scaling approaches, and query optimization techniques.
Proven experience in architecting and implementing data pipelines using native AWS services in a modular and resilient manner.
Solid understanding of data modeling concepts, including dimensional, normalized, and lakehouse patterns.
Good to have skills: Experience of working for customers/workloads in one of FSI,Retail,CPG domain.
Exposure to IaC tools like Terraform and to CI/CD tools.
Familiarity with data virtualization (e.g., Amazon QuickSight,PowerBI, Tableau) and data governance tools (e.g., Collibra).
Managing security, monitoring, and compliance with AWS IAM, Secrets Manager, CloudWatch, CloudTrail, and KMS.
Experience with AI-assisted development tools such as GitHub Copilot, Amazon Kiro (or similar GenAI IDEs) for improving developer productivity, code generation, and pipeline acceleration.
Exposure to Data Mesh architecture,Data Governance frameworks.

Benefits

Make an impact at one of the world’s fastest-growing AI-first digital engineering companies.
Upskill and discover your potential as you solve complex challenges in cutting-edge areas of technology alongside passionate, talented colleagues.
Work where innovation happens - work with disruptive innovators in a research-focused organization with 60+ patents filed across various disciplines.
Stay ahead of the curve—immerse yourself in breakthrough AI, ML, data, and cloud technologies and gain exposure working with Fortune 500 companies.
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us !

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data warehousingdata lakesETLmetadata frameworksAWS GlueAWS LambdaAmazon EMRAWS KinesisSQLPython

Soft Skills

collaborationproject deliverycommunicationteamworkproblem-solving