Senior Data Engineer

Role: Data Engineer

Location: Omaha, Ne (1st preference) or Chicago, IL / relocation workable Hybrid (Tuesday, Wednesday, Thursday in office)

Type: Contract

Experience required: 6+ years

Must Have Skills: Databricks, Snowflake, Pyspark (Hands-on experience in development)

Minimum years of experience required: 5-8 years Certification needed: Not mandatory Must Have Skills: Databricks, Snowflake, Pyspark Nice to Have

Skills: IICS, Python

Skill Set:

As a Senior Data Engineer, you will play a key role in leading the development, maintenance, and optimization of data pipelines and workflows within our Enterprise Data Platform. You’ll apply strong data engineering fundamentals along with software engineering and DevOps practices, so pipelines are built, deployed, and monitored as code. Your work will help ensure data accuracy, reliability, and accessibility, enabling teams across the organization to make informed decisions.

This position offers an opportunity to lead technical solutions, mentor engineers, and collaborate with cross-functional teams to solve complex data challenges and create impactful solutions.

Key Responsibilities:

• Lead the design, development, and maintenance of scalable data pipelines that process and integrate data from multiple sources into the Enterprise Data Platform.

• Build pipelines and workflows as code using modern engineering practices (version control, code reviews, automated testing, reusable components).

• Define and implement patterns for CI/CD for data pipelines (automated builds, tests, deployments, and environment promotion).

• Partner with data scientists, analysts, and business teams to gather requirements and translate them into robust data solutions.

• Build and optimize SQL queries and transformations to support complex business use cases and analytics needs.

• Design and manage data models; validate them with business stakeholders, data architects, and governance partners.

• Establish data quality checks, validation, and troubleshooting practices to ensure accuracy, consistency, and trust in data products.

• Monitor and optimize pipeline performance and reliability; implement observability (logging/metrics/alerts) and contribute to operational runbooks.

• Drive automation to improve efficiency, reduce manual effort, and increase repeatability of platform operations.

• Provide technical leadership through mentoring, reviews, and guidance on best practices and standards.

• Participate in Agile ceremonies to plan, estimate, and deliver work efficiently.

• Create and maintain documentation for data workflows, transformations, standards, and operational procedures.

Technical Skills:

• Bachelor’s degree in computer science, Information Systems, or a related field (or equivalent experience).

• 5–8 years of experience in data engineering or a related role.

• Advanced proficiency in SQL for complex data transformation and analysis.

• Hands-on experience with cloud-based data platforms such as Databricks, Snowflake, or similar tools.

• Experience with ETL/ELT tools and frameworks (e.g., Informatica, Talend, dbt, or equivalent).

• Strong proficiency in Python and/or PySpark for data processing and pipeline development.

• Strong understanding of data modeling, database design principles, and building curated datasets for analytics and operational use cases.

• Experience with DevOps practices and Git-based development (branching strategies, pull requests, code reviews).

• Experience implementing CI/CD for data pipelines/workflows and managing deployments across environments.

• CPG Domain Knowledge will be a plus.

• Familiarity with orchestration and workflow tools (e.g., Databricks Workflows, Airflow, or similar) is preferred.

• Familiarity with Infrastructure as Code (e.g., Terraform, CloudFormation) and/or containerization concepts is a plus.

• Strong problem-solving skills, attention to detail, and ability to troubleshoot complex issues end-to-end.

• Excellent communication skills and ability to collaborate across technical and non-technical teams.