Data Engineer

Veolia Environnement SA•Paramus, NJ

4d•$169,541 - $175,000•Hybrid

About The Position

The Data Engineer is responsible for designing, building, and maintaining scalable data pipelines and infrastructure to support enterprise analytics, reporting, and AI initiatives. This role ensures the availability, quality, and reliability of data by integrating diverse sources into secure and optimized data platforms. Working closely with data scientists, analysts, and business stakeholders, the Data Engineer enables data-driven decision-making across the organization.

Requirements

MS degree in Computer Science or computer related field from an accredited institution.
3+ years hands proven experience as a Data Engineer or similar role.
More than 2 years of experience developing with Python.
1+ year performing with production environments in a DevOps culture managing code composed of multi-developer teams, following industry best practices.
2+ years SQL development experience.
1+ year experience with data modeling
1+ year bash scripting experience.
1+ year experience with Git, CI/CD (preferably GitLab) and Docker.
2+ years experience deploying and running services in Cloud Big Data platforms such as BigQuery and Snowflake.
1+ year experience with DAG workflows orchestration such as Apache Airflow, Prefect.
Experience with designing and building data pipelines using tools like Apache Beam, CDAP (Data fusion).
1+ year experience with CDC design patterns and their challenges.

Nice To Haves

Experience with NoSQL databases is a plus (i.e Firestore, MongoDB).
Experience designing and developing APIs is a plus (i.e using FastAPI, Flask)
Embrace mentorship through design sessions, code reviews, and community building.
Take ownership and support solutions you develop.
Value collaboration with other members of the team.
Have a product mindset.
Good communication.

Responsibilities

Develop and operate data management tools, monitoring data flows, data quality, data cleansing and data processing.
Create and document logical data integration (ETL) strategies for data flows between disparate systems and the enterprise data warehouse/data lakes.
Collaborate with different stakeholders (engineers, data owners) to collect required data from internal and external systems.
Work in an Agile environment that focuses on collaboration and teamwork.
Improve and extend existing data infrastructure services.
Monitor production job schedule and correct job failures in a timely manner.