Data Platform Architect

We are seeking a hands-on Data Platform Architect to lead the design, implementation, and modernization of our enterprise-wide data platform—including data governance, lakehouse architecture, engineering pipelines, analytics, and AI-driven solutions. This role requires deep technical expertise, strategic vision, and executional leadership to build a scalable, governed, and intelligent data ecosystem across cloud and on-prem environments.

Responsibilities

Define and continuously evolve the target data architecture across the stack—governance, engineering, modeling, lakehouse, AI/ML; Translate business and technical goals into scalable and resilient platform designs; Own and maintain architectural roadmaps, standards, and decision frameworks; Act as the bridge between architects, Business SME/Analysts, data engineers, and analytics teams to ensure alignment and compliance with platform standards; Design and implement modern ELT/ETL pipelines using tools like Spark, Python, SQL, Scala, and cloud-native components (e.g., Fivetran, Databricks, Snowflake, BigQuery); Proven Hand-on experience in Databricks & Unity Catalogue; Build and maintain Lakehouse platforms using Delta Lake, Iceberg, or equivalent technologies; Manage data ingestion from heterogeneous sources including ERP, CRM, IoT, and third-party APIs; Guide hands-on development of robust, reusable, and automated data flows; Implement and enforce data governance frameworks including data lineage, metadata management, and access controls; Partner with Data Stewards and Governance Analysts to catalog data domains, define entities, and ensure SOX compliance; Drive adoption of tools like and Atlan/Unity Catalog for metadata, quality, and stewardship; Develop data models (ERDs, dimensional and 3NF) and define canonical data representations; Review solution designs and provide architectural guidance to engineering teams; Mentor technical staff while fostering best practices and continuous improvement; Collaborate with DevOps to embed CI/CD, version control, and environment automation across the data lifecycle; Continuously assess and improve platform reliability, scalability, performance, and cost-efficiency

Requirements

10+ years of hands-on experience in data architecture, engineering, and analytics delivery; Proven success in building modern data platforms on cloud (AWS, Azure, GCP); Deep knowledge of data lakehouse architectures (e.g., Databricks, Fabric); Proficiency with Python, SQL, Spark, and orchestration frameworks; Experience with ETL/ELT tools (e.g., Informatica, Talend, Fivetran) and containerization (Docker, Kubernetes); Strong background in Data Modeling (ERD, star/snowflake, canonical models); Familiarity with REST APIs, GraphQL, and event-driven design; Demonstrated experience integrating AI/ML and GenAI components into data platforms

Nice to have

10+ years of experience including solution architecture roles for large-scale data initiatives; Deep expertise in Databricks, Delta Lake, Unity Catalog and Azure Data Services; Experience with BI/visualization tools like Power BI, Tableau, and Looker; Understanding of Master Data Management, data profiling, cleansing, and enrichment techniques; Exposure to DataOps and DevOps practices for CI/CD and platform automation; Strong analytical and problem-solving skills with the ability to communicate clearly to both technical and business audiences; Knowledge of high-tech business domains such as engineering, sales, finance, supply chain, or semiconductor operations is a strong plus; Knowledge of SAP S/4 Hana ERP and SAP BDC is a plus