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

- (Experience 2) Strong hands-on experience designing and implementing Medallion Architecture (Bronze / Silver / Gold layers) on AWS-based data platforms

- (Experience 3) Strong hands-on experience with Snowflake or Databricks, including data modelling, performance optimization, transformation pipelines, and scalable data warehouse implementations

- (Experience 4) Must have strong expertise in advanced SQL including complex joins, CTEs, window functions, query optimization, and performance tuning

- (Experience 5) Must have Hands-on development experience with Python and Apache Spark/PySpark for large-scale data transformation and processing pipelines

- (Experience 6) Strong experience working with AWS data services including S3, Glue, Athena, and cloud-native analytics/data lake architectures

- (Experience 7) Strong experience in end-to-end ETL/ELT pipeline development including ingestion, transformation, reconciliation, validation, testing, deployment, and production support

- (Experience 8) Experience working across transaction-heavy enterprise domains such as Finance, Supply Chain, HR, Customer, or Operations datasets

- (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

- (Frameworks) Familiarity with APQC, SCOR, or enterprise process modelling frameworks is an added advantage