What does a Data Engineer do?

A Data Engineer builds and maintains the pipelines and infrastructure that move data from source systems into a warehouse where it can be used. They ingest, transform, and model data, then make sure it is accurate, fresh, and reliable. Their work is what lets analysts and scientists trust the numbers.

What are the main responsibilities of a Data Engineer?

The core duties are building and maintaining data pipelines, modeling the warehouse, and ensuring data quality and availability. Most data engineers also orchestrate workflows, optimize cost and performance, and set up governance so data is secure and well documented.

What skills does a Data Engineer need?

Strong SQL and programming in Python, Scala, or Java are essential, along with experience in ETL or ELT, data modeling, and a cloud data warehouse. Familiarity with orchestration tools like Airflow and transformation tools like dbt is important, as is a disciplined focus on reliability and testing.

What should a Data Engineer job description include?

A strong Data Engineer job description starts with a one-line definition and brief, then lists key responsibilities, full duties, and required experience and skills. Specifying your data stack, warehouse, and the scale of data you handle helps candidates judge whether the role matches their background.

Who does a Data Engineer work with?

Data Engineers work closely with analysts and data scientists who consume the datasets they build, and with software engineers who own the source systems. They also coordinate with platform and DevOps teams on infrastructure and with leadership on data strategy and governance.

Data & AI

Data Engineer Job Description

Data Analyst Data Scientist Machine Learning Engineer Business Analyst AI Engineer Data Architect Business Intelligence (BI) Analyst Database Administrator (DBA)MLOps Engineer

About the role

We are hiring a Data Engineer to build the backbone that every analyst, scientist, and dashboard depends on. You will design pipelines, model the warehouse, and make sure data arrives on time, accurate, and well documented. If you take pride in systems that just work and data people can trust, this role is for you.

Key responsibilities

Build and maintain reliable data pipelines
Model and manage the data warehouse
Ensure data quality, freshness, and availability

Responsibilities

Design and build batch and streaming pipelines that ingest data from many sources
Model the data warehouse with clean, well-documented, and performant schemas
Develop and maintain transformation layers using tools like dbt or Spark
Orchestrate workflows with schedulers such as Airflow and manage dependencies
Implement data quality checks, tests, and alerting to catch issues before users do
Optimize storage, partitioning, and queries to control cost and improve speed
Manage ingestion connectors and APIs, handling schema changes gracefully
Build and enforce data governance, access controls, and lineage tracking
Partner with analysts and scientists to expose clean, trusted datasets for their work
Monitor pipeline health and resolve failures and backfills quickly

Requirements

Bachelor's degree in computer science, engineering, or a related technical field
3 or more years building data pipelines and warehouse infrastructure
Strong SQL and programming skills in Python, Scala, or Java
Hands-on experience with a cloud data warehouse and an orchestration tool
A track record of delivering reliable, well-tested data infrastructure

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