Senior Data Engineer

Capgemini•New York, NY

8h•Hybrid

About The Position

Data engineers are responsible for building reliable and scalable data infrastructure that enables organizations to derive meaningful insights, make data-driven decisions, and unlock the value of their data assets.Job Description - Grade SpecificThe involves leading and managing a team of data engineers, overseeing data engineering projects, ensuring technical excellence, and fostering collaboration with stakeholders. They play a critical role in driving the success of data engineering initiatives and ensuring the delivery of reliable and high quality data solutions to support the organizations data driven objectives.Capgemini is an AI-powered global business and technology transformation partner, delivering tangible business value. We imagine the future of organizations and make it real with AI, technology and people. With our strong heritage of nearly 60 years, we are a responsible and diverse group of 420,000 team members in more than 50 countries. We deliver end-to-end services and solutions with our deep industry expertise and strong partner ecosystem, leveraging our capabilities across strategy, technology, design, engineering and business operations. The Group reported 2024 global revenues of €22.1 billion.Make it real | www.capgemini.com

Requirements

4 to 8+ years of data engineering experience developing large data pipelines in very complex environments
Very Strong SQL skills and ability to build Very complex transformation data pipelines using custom ETL framework in Google BigQuery environment
Exposure to Teradata and ability to understand complex Teradata BTEQ scripts
String Python programming skills
Strong Skills on build Airflow Jobs and Debug issues
Ability to Optimize the Query in BigQuery
Hands-on experience on Google Cloud data Technologies (GCS , BigQuery, Dataflow, Pub sub, Data Fusion , Cloud Function)

Nice To Haves

Experience with cloud data warehouse technology BigQuery.
Nice to have experience with Cloud technologies like GCP (GCS , Data Proc, Pub/sub, Data flow, Data Fusion, Cloud Function)
Nice to have exposure to Teradata
Solid experience with Job Orchestration Tools like Airflow and ability to build complex Jobs.
Writing and maintaining large Data Pipelines using Custom ETL framework
Ability to Automate Jobs using Python
Familiarity with Data Modeling techniques and Data Warehousing standard methodologies and practices
Very good experience with Code Version control repository like Github
Good Scripting skills, including Bash scripting and Python
Familiar with Scrum and Agile methodologies
Problem solver with strong attention to detail and excellent analytical and communication skills
Ability to work in Onsite / Offshore model and able to lead a Team.

Responsibilities

Contribute to the migration of legacy data warehouse to a google cloud-based data warehouse
Collaborate with Data Product Managers, Data Architects to design, implement, and deliver successful data solutions
Help architect data pipelines for the underlying data warehouse and data marts
Design and develop very complex ETL pipelines in Google cloud Data environments.
Maintain detailed documentation of your work and changes to support data quality and data governance
Support QA and UAT data testing activities
Support Deployment activities to higher environments
Ensure high operational efficiency and quality of your solutions to meet SLAs and support commitment to our customers (Data Science, Data Analytics teams)
Be an active participant and advocate of agile/scrum practice to ensure health and process improvements for your team

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