Senior Data Engineer

Tech Partners; Tulsa (OK); On-site; USD 120,000 - 160,000; Full time

Job summary

Tech Partners in Tulsa is seeking a Senior Data Engineer to own the ELT pipelines for analytics and reporting in a large financial services setting.

This hands-on lead writes code, sets patterns, reviews architecture, and mentors engineers. All work runs on AWS with Terraform-driven infrastructure and Snowflake-based data models for scalable, auditable pipelines.

Qualifications

• 7 to 8+ years of hands-on data engineering in a heavy AWS environment.
• Experience with Snowflake architecture and performance tuning at enterprise scale.
• Fivetran ingestion, CDC, and schema evolution experience.
• Terraform for cloud infra and Snowflake object management.
• dbt in production with modular models, tests, and docs.

Responsibilities

• Build and tune Fivetran ingestion and CDC from DB2 and other sources.
• Shape Snowflake architecture: warehouses, isolation, clustering, access control.
• Own CI/CD path for data assets from linting to prod promotion.
• Write Python, PySpark, and SQL when dbt isn’t suitable; mentor engineers.
• Collaborate with security/compliance to maintain controls without slowing the team.

Skills

Leadership

Mentoring

Stakeholder communication

Problem solving

Education

Bachelor's in data-centric field

Tools

Snowflake

Fivetran

Terraform

dbt

Python

PySpark

SQL

CI/CD

DB2

AWS

Job description

About the job Senior Data Engineer - Tulsa, OK

Senior Data Engineer
Contract to Hire | Tulsa, OK

What We're Actually Solving

Data is coming out of DB2 and a long list of other source systems, landing in Snowflake through Fivetran, and getting shaped by dbt into something the business can trust. The pieces are in place. What's missing is the senior engineer who sets the standard for how it all gets built, deployed, and run, so pipelines stop behaving like one‑off projects and start behaving like a platform.

The Role

You'll own the design and governance of the ELT pipelines that feed analytics and reporting across a large financial services organization. This is a hands‑on technical lead seat. You write the code, set the patterns others follow, review architecture, and mentor the engineers around you. Everything runs on AWS and infrastructure lives in Terraform. When a pipeline breaks, a Snowflake bill spikes, or a new tool needs a yes or no, you're the one people look to.

Day to Day

Build and tune Fivetran ingestion and CDC out of DB2 and other sources, handle schema changes without breaking downstream models, and hold the line on ingestion SLAs. Structure dbt projects into staging, intermediate, and mart layers, write the macros and tests that keep them consistent, and make lineage and documentation something people actually use.

Shape the Snowflake architecture: warehouse sizing, workload isolation, clustering, access control, secure sharing, and the cost conversation nobody else wants to have. Provision Snowflake objects and AWS infrastructure through Terraform so every environment is repeatable and auditable.

Own the CI/CD path for data assets, from linting and automated validation through dev, test, and prod promotion that compliance can trace. Write Python, PySpark, and SQL where dbt isn't the right tool, and set the standards the team writes to. Run proofs of concept on new tooling and make the call on what earns a spot in the stack. Work with security and compliance so controls hold without slowing the team to a crawl.

What We're Looking For

Someone who has already built this kind of platform and has opinions about what they'd do differently next time. You can explain why you sized a warehouse the way you did, why a model lives in intermediate instead of a mart, and what a bad schema change cost you once. You spot problems before anyone asks, and you can walk a business stakeholder through a technical tradeoff without losing them.

Experience That Matters

• 7 to 8+ years of hands‑on data engineering, with real production time in a heavy AWS environment (required)
• Snowflake architecture and performance tuning at enterprise scale
• Fivetran connector configuration, CDC, and schema evolution
• Terraform for cloud infrastructure and Snowflake object management
• DB2 as a source system: you know how data comes out of it and where it bites
• dbt in production: modular models, macros, tests, documentation, version control
• CI/CD for data pipelines and a history of leading or mentoring engineers
• Bachelor's in a data-centric field or equivalent experience

Nice to Have

Kafka or other streaming work, PySpark at scale, Azure exposure, and time in banking or another regulated industry.