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# Data Engineer

- **Company:** Kiewit
- **Location:** Omaha, NE, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Sql Data Warehouse, Adobe InDesign, Artificial Intelligence, Airflow, Business Analytics Applications, Data Analysis, Automation of Tests, Microsoft Azure, Code Review, Databases, Continuous Integration, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Transformation, Data Structures, Data Systems, Data Warehousing, Distributed Computing Environment, Github, Python (Programming Language), Metadata, Operational Databases, Performance Tuning, Software Engineering, SQL Databases, Data Streaming, Snowflake, Grafana, Apache Spark, Analytic Functions, Git, Data Lakes, Deployment Automation, Data Management, Software Version Control, Data Pipelines, Databricks
- **Published:** November 6, 2026
- **Apply:** https://dejobs.org/x/x/FF3345EDA0CD4F22B33A5279D6E3538C/job/

## About the Role

· 3+ years of experience in data engineering, analytics engineering, software engineering, or a related discipline, or equivalent practical experience.

· Advanced experience with Python and SQL.

· Demonstrated experience designing and operating scalable batch or streaming data pipelines.

· Strong understanding of data lake, lakehouse, cloud data warehouse, and dimensional data modeling concepts.

· Experience with, or demonstrated ability to quickly learn, Microsoft Azure, Databricks, and Snowflake.

· Experience with, or strong interest in learning, Dagster and dbt.

· Understanding of distributed data processing, data partitioning, schema evolution, and performance optimization.

· Experience implementing automated testing, monitoring, alerting, and operational controls for production data systems.

· Familiarity with source control, pull-request reviews, CI/CD, and automated deployment practices.

· Ability to lead technical discussions and communicate complex topics clearly to technical and nontechnical audiences.

· Demonstrated ability to work effectively across engineering, analytics, security, governance, and business teams.

· Strong problem-solving, ownership, prioritization, and decision-making skills.

Preferred Qualifications

· Experience developing analytics solutions with dbt, including testing, documentation, deployment, and model optimization.

· Experience building or operating workflows with Dagster, Airflow, or a comparable orchestration platform.

· Experience with Azure DevOps or GitHub-based CI/CD workflows.

· Experience with Grafana or similar monitoring and observability platforms.

· Experience supporting data platforms in secure or large-enterprise environments.

· Experience using AI-assisted software engineering tools securely and effectively.

· Demonstrated experience mentoring engineers or leading technical initiatives. #LI-AK1

Other Requirements:

 * Regular, reliable attendance
 * Work productively and meet deadlines timely
 * Communicate and interact effectively and professionally with supervisors, employees, and others individually or in a team environment.

## Description

The Data Engineer is responsible for the development of ELT/ETL packages and scripts to move data from source applications to data Platforms for Reporting and Analytic functions, · Build and support data pipelines that ingest, transform, validate, and publish data for analytics and enterprise data products.

· Implement data solutions across lakehouse, data lake, and cloud data warehouse platforms, following established architecture and design patterns.

· Develop reusable datasets and data transformations using technologies such as Databricks, Spark, Snowflake, and dbt.

· Follow team standards for data modeling, coding, testing, documentation, monitoring, and deployment.

· Configure and support scheduled data workflows using JAMS, Dagster, or similar orchestration platforms.

· Implement data-quality checks for completeness, accuracy, freshness, schema changes, and business rules.

· Profile source data and collaborate with data modelers, DBAs, and technical leads to identify data structures, performance needs, and required database changes.

· Apply Git-based development and established CI/CD practices to develop, test, document, schedule, and deploy data pipelines.

· Contribute to data platform practices for security, governance, metadata, lineage, reliability, and recovery.

· Work with product owners, architects, platform engineers, analysts, data scientists, and business stakeholders to translate requirements into maintainable data solutions.

· Coordinate with data producers and consumers to clarify data requirements, dependencies, ownership, and expectations.

· Produce and maintain solution documentation as a standard deliverable, including data flows, transformation logic, dependencies, operational procedures, and support guidance.

· Troubleshoot production issues, support business-critical workflows, and document findings and resolutions to help prevent recurring problems.

· Participate in production support, on-call duties, agile delivery activities, and post-deployment knowledge sharing.

· Participate in design and code reviews, incorporating feedback to improve the quality, performance, and maintainability of data solutions.

· Share technical knowledge with teammates and contribute to team learning and continuous improvement.

· Evaluate tools and approaches relevant to the team's data platform, and recommend improvements through established technical review processes.

· Use AI-assisted development tools responsibly to support engineering, testing, and documentation.