Data Architect - Medallion Architecture

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 outputsIdeal 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 consideredPreferred : - (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 (ref:hirist.tech)