Join Kiewit’s Data Platform team to help build and maintain data pipelines that deliver analytics-ready tables.

Write and maintain SQL transformations and Python scripts for data ingestion and transformation workflows
Support ETL/ELT pipeline development and testing under the guidance of senior engineers
Contribute to code reviews, documentation, and testing practices that improve data trustworthiness
Collaborate with analysts, data architects, and business stakeholders to understand requirements and troubleshoot data issues
Learn and apply Kiewit’s data stack, including a cloud lake house plus orchestration and transformation tooling
Surface blockers, ask questions, and propose solutions as part of a collaborative team

0–3 years of relevant experience (internships, academic or personal projects, or early-career work in data, analytics, or software)
Demonstrable experience through internships or project coursework involving data, analytics, and business stakeholders
Degree in Computer Science / Computer Science & Engineering, Data Science, or similar
SQL proficiency: basic joins, aggregations, and database normalization concepts
Python (or similar): comfortable writing scripts, using libraries like pandas, and understanding functions and OOP basics
Git/GitHub: used version control in a class project or personal repository; understands branches and PRs at least conceptually
High-level understanding of data pipelines, including ETL/ELT and batch vs. streaming
Comfort with the command line and navigating basic cloud/dev environments
Strong written communication skills to support documentation and async collaboration

Top-tier medical, dental, and vision plans covering eligible employees and dependents
Voluntary wellness and employee assistance programs
Life insurance
Disability
Retirement plans with matching
Generous paid time off

Regular, reliable attendance
Work productively and meet deadlines timely
Communicate and interact professionally with supervisors, employees, and others individually or in a team environment
Perform work safely and effectively; follow oral and written instructions, including warning signs, equipment use, and other policies
Work during normal operating hours to organize and complete work within given deadlines; overtime and weekends as required

Exposure to a cloud platform such as Azure or GCP
Familiarity with modern data tooling such as Databricks, dbt, Snowflake
Experience with data quality or testing frameworks
Coursework or project experience involving data warehousing or dimensional modeling

Data Engineer 1

Job Description