Role & Responsibilities
- Own end-to-end data architecture across Medallion layers (Bronze/Silver/Gold) on AWS
- Design atomic star schema data models (fact and dimension tables) across multiple business domains
- Define and implement aggregation and KPI layers aligned to business reporting requirements
- Establish data modelling standards, naming conventions, and design patterns across teams
- Ensure alignment with enterprise data strategy and roadmap
- Translate business processes into scalable and reusable data models
- Write and review SQL, Python, and Spark code for pipelines and transformations
- Build and validate data models in Snowflake or Databricks
- Deliver pipelines using AWS services (S3, Glue, Athena)
- Perform data reconciliation and validation across layers (source → curated → BI)
- Ensure data quality, consistency, and integrity across the pipeline
- Own end-to-end SDLC (requirements → design → build → test → release → operate)
- Lead and unblock data engineers and analysts on day-to-day delivery
- Identify blockers, dependencies, and risks early, and propose solutions
- Align delivery with programme roadmap and milestones
- Track execution using Jira and maintain documentation in Confluence
- Gather and validate requirements across multiple business domains
- Translate business requirements into technical designs and data models
- Communicate architecture decisions clearly to non-technical stakeholders
- Drive cross-team alignment to ensure consistent, reliable, and trusted outputs
Ideal Candidate
- Strong Data Architect Profile (AWS / Medallion Architecture / Analytics Transformation)
- Mandatory (Experience 1) – Must have 8+ years of experience in Data Engineering / Data Architecture with strong exposure to large-scale analytics transformation programmes
- Mandatory (Experience 2) – Strong hands-on experience designing and implementing Medallion Architecture (Bronze / Silver / Gold layers) on AWS-based data platforms
- Mandatory (Experience 3) – Strong hands-on experience with Snowflake or Databricks, including data modelling, performance optimization, transformation pipelines, and scalable data warehouse implementations
- Mandatory (Experience 4) – Must have strong expertise in advanced SQL including complex joins, CTEs, window functions, query optimization, and performance tuning
- Mandatory (Experience 5) – Must have Hands-on development experience with Python and Apache Spark/PySpark for large-scale data transformation and processing pipelines
- Mandatory (Experience 6) – Strong experience working with AWS data services including S3, Glue, Athena, and cloud-native analytics/data lake architectures
- Mandatory (Experience 7) – Strong experience in end-to-end ETL/ELT pipeline development including ingestion, transformation, reconciliation, validation, testing, deployment, and production support
- Mandatory (Experience 8) – Experience working across transaction-heavy enterprise domains such as Finance, Supply Chain, HR, Customer, or Operations datasets
- Mandatory (Note) – Only immediate joiners or candidates who can join within 15 days will be considered
- Preferred (Experience) – Experience working with ERP / enterprise systems such as SAP, Oracle, Salesforce, or similar enterprise platforms
- Preferred (Frameworks) – Familiarity with APQC, SCOR, or enterprise process modelling frameworks is an added advantage
Skills: enterprise,data models,aws,analytics,medalliion,data,architecture,transformation
