Live opening · Posted 5 days ago
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Description supplied by the original job listing.
We are seeking a hands-on Senior Staff Engineer, Enterprise Data Platform 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.
ESSENTIAL DUTIES AND RESPONSIBILITIES:
Architecture & Strategy
Define and continuously evolve the target data architecture across the stack—Data Governance, data engineering, data modeling and Databricks lakehouse in Azure and GCP
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
Build and maintain Databricks Lakehouse platforms using Delta Lake, Iceberg, or equivalent technologies.
Design and implement modern ELT/ETL pipelines using tools like Spark, Python, SQL, Scala, and cloud-native components (e.g., Fivetran, Databricks, Snowflake, BigQuery).
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 in Azure and GCP
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 Atlan and Unity Catalog for metadata, quality, and stewardship.
Develop data models (ERDs, dimensional and 3NF) and define canonical data representations.
Define and maintain Data Products including end to end data flows, Data Lineage, Data Access and Data Catalog
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
REQUIRED:
9+ 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.
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 and SAP-BDC a plus
Employment type
Full-time
Work arrangement
No
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