Data Platform Architect

hands-on Data Platform Architect

ESSENTIAL DUTIES AND RESPONSIBILITIES:

Architecture & Strategy

• 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.

Data Engineering & Platform Delivery

• Design and implement modern ELT/ETL pipelines using tools like Spark, Python, SQL, Scala, and cloud-native components (e.g., Fivetran,

  Databricks

  , Snowflake, BigQuery).
• 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.

Governance, Metadata, and Quality

• 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.

AI & Agentic Enablement

• Lead the integration of AI/ML solutions and agentic architectures into the data platform to automate insights, recommendations, and decision flows.
• Collaborate with AI/ML teams to embed vectorization, tokenization, and semantic enrichment into the data stack.
• Prototype innovative GenAI applications for operational and business intelligence.

Collaboration & Leadership

• 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.Qualifications

REQUIRED:

• 12+ 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.

PREFERRED:

• 15+ 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 ERP and SAP BDC is a plus