Data Architect - AWS/Medallion Architecture

Description :

- 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