Data Engineer

Colgate•Piscataway, NJ

6h•Hybrid

About The Position

We are seeking a passionate and detail-oriented Data Engineer to join our Digital Tech & Data team. In this role, you will be responsible for building and maintaining the data pipelines that support our digital ecosystem — from ingesting raw data across multiple digital touchpoints to delivering clean, robust data that business teams can trust and act on. You will bring engineering rigour to data — treating pipelines, models, and infrastructure as production-grade software — while staying closely connected to the digital business context your work enables.

Requirements

Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or a related discipline.
2–3 years of hands-on experience in a data engineering or analytics engineering role, ideally within a digital or e-commerce environment.

Nice To Haves

Proven experience building and maintaining production-grade data pipelines using Apache Airflow.
Strong working knowledge of Snowflake, including data modelling, performance tuning, clustering, and warehouse cost management.
Demonstrated experience developing dbt projects — writing modular, tested, and well-documented transformation logic.
Practical experience using Terraform to provision and manage cloud data infrastructure in a repeatable, version-controlled manner.
Proficiency in Python for writing data pipelines, custom operators, and utility tooling.
Strong SQL skills with the ability to write and optimise complex queries across large datasets.
Fluency in driving an AI coding tool across pipeline codebase: scoping changes, providing the right files as context.

Responsibilities

Design, build, and maintain production-grade data pipelines in Airflow that ingest data from digital touchpoints into Snowflake.
Develop modular, tested, and well-documented dbt models that transform raw data into reliable, business-ready datasets — owning the full lifecycle from source definition to exposure.
Provision and manage cloud data infrastructure (Snowflake objects, Airflow environments, supporting GCP resources) through Terraform, with everything version-controlled and peer-reviewed.
Implement and uphold data quality, observability, and testing standards across pipelines
Tune Snowflake performance and manage warehouse cost — clustering, query profiling, resource monitors and treat cost as a first-class engineering concern.
Implement pipelines to platform standards — branching strategy, CI/CD for dbt and Airflow, code review norms, documentation, naming conventions