Data Engineer

Capgemini•New York, NY

11d•$80,786 - $90,273•Hybrid

About The Position

We are seeking a highly motivated Data Engineer to design, develop, and maintain scalable data solutions that support analytics, reporting, and business operations. The ideal candidate will have strong experience in building data pipelines, data warehouses, and modern data platforms while ensuring data quality, governance, security, and performance across the enterprise.

Requirements

Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.
5+ years of experience in Data Engineering, Data Warehousing, or Data Integration.
Strong experience with ETL/ELT development and data pipeline design.
Proficiency in SQL and at least one programming language such as Python, Java, or Scala.
Experience with relational and NoSQL databases.
Strong understanding of data modeling, data warehousing, and modern data architectures.
Experience working with cloud-based data platforms and big data technologies.
Knowledge of data governance, data quality, and security best practices.
Strong analytical, problem-solving, and communication skills.

Nice To Haves

Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
Experience with modern data lake and lakehouse architectures.
Familiarity with Spark, Databricks, Snowflake, or similar data technologies.
Experience with CI/CD, DevOps, and workflow orchestration tools.

Responsibilities

Design, develop, and maintain robust ETL/ELT pipelines for structured and unstructured data processing.
Build scalable and reliable data solutions to support analytics, reporting, and operational use cases.
Develop and optimize data models, database schemas, and storage structures.
Integrate data from multiple internal and external systems and applications.
Develop, maintain, and enhance data warehouses, data lakes, and lakehouse platforms.
Implement data validation, cleansing, transformation, and quality assurance processes.
Ensure data integrity, governance, security, and compliance across enterprise data platforms.
Monitor, troubleshoot, and optimize data pipelines to improve performance, scalability, and reliability.
Automate data workflows, scheduling, and operational processes.
Support data migration, modernization, and cloud transformation initiatives.
Collaborate with Data Scientists, Business Analysts, Product Owners, and Application Development teams to deliver business-driven data solutions.
Document data architectures, data flows, technical designs, and operational procedures.
Participate in code reviews and promote engineering best practices, standards, and continuous improvement.