Senior Tool / Platform Engineer

Capgemini•New York, NY

12d•$125,000 - $135,000•Hybrid

About The Position

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.

Requirements

Bachelor's degree in Computer Science, Engineering, or related discipline.
10 to 15+ years of experience in data engineering and/or data architecture roles.
Advanced hands-on experience with Databricks, Apache Spark, and the Azure data stack.
Strong practical knowledge of Lakehouse patterns, Delta Lake, and modern data warehousing.
Demonstrated background designing resilient ETL/ELT systems with both batch and streaming patterns.
Proficiency with SQL data stores, schema design, and performance optimization.
Experience working with structured and semi-structured formats such as Parquet, ORC, and JSON.
Solid understanding of cloud security, access governance, and metadata/catalog strategies (e.g., Unity Catalog).
Experience implementing automated build/deploy workflows for data solutions.
Excellent analytical, problem-solving, and communication skills.

Nice To Haves

Hands-on experience with data lineage tools, or governance frameworks.
Exposure to domain-driven design or product-oriented data architecture.

Responsibilities

Develop end‑to‑end architectural patterns leveraging Databricks, Spark, Delta Lake, Unity catalog and Azure Data services
Design scalable Lakehouse foundations, ensuring performance, reliability, and maintainability of data layers.
Establish architectural frameworks and standards for ingestion, curation, storage, and consumption.
Experience in building a data platform with unity catalog and design the multi-tenant solution with data governance principles.
Serve as a primary technical advisor for engineering teams, analysts, and data science partners.
Participate in planning ceremonies, architectural councils, and cross-functional strategy discussions.
Continuously evaluate emerging tools, Databricks capabilities, and Azure offerings to enhance platform maturity.
Maintain and refine coding conventions, DevOps standards, and operational playbooks.
Drive automation across deployment processes using Azure DevOps, or equivalent CI/CD toolchains.