Data Engineer

DataJobs; Omaha (NE); On-site; USD 80,000 - 140,000; Full time

Benefits offered by this job

401(k) matching

Dental insurance

Health insurance

Life insurance

Paid time off

Professional development assistance

Retirement plan

Vision insurance

Job summary

Todata Analytics in Omaha, NE is hiring a Data Engineer for our onsite team. You will build data ingestion and transformation pipelines and design data models to support products and analytics with governed data in a regulated environment.

You will work on data governance, data quality and lineage, and collaborate with product and software teams to deliver well-modeled, usable data. Onsite in Omaha, you will maintain CI/CD practices and environment separation.

Qualifications

• 3+ years of data engineering experience with hands-on Databricks (Spark, Delta Lake, Unity Catalog).
• Strong SQL skills: complex queries, joins, window functions, performance optimization.
• Solid Python experience for data processing and scripting (PySpark, pandas).
• Hands-on Databricks with notebooks, Delta Lake, jobs, and clusters.

Responsibilities

• Build and maintain data ingestion and transformation pipelines across the platform.
• Design and evolve data models used by products and analytics.
• Support the infrastructure delivering governed data to downstream consumers, including AI products.

Skills

Databricks experience

SQL

Python scripting

PySpark

Pandas

Data governance

CI/CD

Data modeling

Multi-tenant isolation

Troubleshooting

Tools

Databricks

Delta Lake

Unity Catalog

Spark

Job description

Todata Analytics is hiring a Data Engineer for our Omaha, NE onsite team. This role supports governed data ingestion and transformation pipelines that help power products and AI in a regulated environment. You will focus on data governance, data quality and lineage, and the engineering fundamentals that keep data reliable and secure.

What you’ll build

• Build and maintain data ingestion and transformation pipelines across the platform.
• Design and evolve the data models used by products and analytics.
• Support the infrastructure that delivers governed data to downstream consumers, including AI products.

How you’ll protect and improve data

• Implement data governance and access controls suitable for regulated environments, ensuring client data is correctly isolated.
• Monitor data quality and lineage and respond to issues before they reach clients.

Engineering fundamentals and collaboration

• Maintain engineering fundamentals such as CI/CD, consistent naming conventions, and environment separation.
• Partner with product and software development to translate client requirements into well-modeled, usable data.
• Independently deliver foundational infrastructure work without close supervision.
• Document what you build and communicate clearly in written form.

What you bring

• 3+ years in data engineering with hands‑on Databricks experience (Spark, Delta Lake, Unity Catalog).
• Strong SQL skills: complex queries, joins, window functions, and performance optimization, including designing dimensional/star‑schema models from ambiguous requirements.
• Solid Python experience for data processing and scripting (for example PySpark and pandas).
• Hands‑on Databricks experience with notebooks, Delta Lake, jobs, and clusters.
• Excellent debugging and problem‑solving skills, tracing issues through logs, code, and data to find root causes.
• Demonstrated care for data governance and multi‑tenant isolation, including how datasets are separated and permissioned.
• Experience with CI/CD, version control, and disciplined naming and environment conventions.

Technology stack

SQL, Python, PySpark, pandas, Databricks, Spark, Delta Lake, Unity Catalog, CI/CD

Compensation and benefits

USD 80,000 - 140,000 per year. Onsite in Omaha, NE.

• 401(k) matching
• Dental insurance
• Health insurance
• Life insurance
• Paid time off
• Professional development assistance
• Retirement plan
• Vision insurance

Nice to have

• Experience in a HIPAA, SOC 2, or other regulated data environment.
• Familiarity with healthcare/clinical research or financial/accounting data domains.
• Exposure to enabling AI/LLM consumers of a governed semantic layer.
• Databricks certification.