Sr. Staff Systems Engineer

Fivetran; Michigan; On-site; USD 180,000 - 240,000; Full time

Job summary

Fivetran is seeking a Senior Staff Systems Engineer, Data and Integrations to own canonical data models and drive large-scale data initiatives. You will work across Systems Engineering, Analytics, and Eng to move data reliably between marketing, sales, billing, and product systems.

You will mentor engineers, shape integration standards, and ensure data quality and governance across critical business processes.

Qualifications

• A data engineering background with expertise in data modeling, ETL/ELT, data quality testing, and data validation at scale.
• Experience with dbt or a similar transformation framework and with API-based or event-driven integration patterns.
• Ability to design canonical data models and promote data governance across multiple systems.

Responsibilities

• Own canonical data models for marketing, sales, billing and product operations integrations.
• Define standards for data modeling, quality, testing, governance, and documentation across teams.
• Lead design of integration architecture and guidance on batch, real-time and event-driven movement.
• Lead complex data initiatives including migrations, consolidations, and re-implementation of core integrations.
• Develop SQL and Python transformations and pipelines with a focus on quality.
• Create validation and reconciliation approaches for migrations and pipeline changes.
• Build monitoring and data quality framework for end-to-end data health across systems.

Skills

Data modeling

SQL

Python/TypeScript

ETL/ELT

Data quality testing

Data validation

Governance

Documentation

Tools

Salesforce

Netsuite

Coupa

Workday

Zendesk

BigQuery

Celigo

dbt

Job description

From the company’s founding until now, our mission has remained the same: to make access to data as simple and reliable as electricity. With the company, customer data arrives in their warehouses, canonical and ready to query, with no engineering or maintenance required. We’re proud that more organizations continue to leverage our technology every day to become truly data-driven.

About Us

the company and dbt Labs are bringing together two industry-leading companies with a shared mission: helping organizations unlock the full value of their data. Together, we’re delivering the data infrastructure layer that helps organizations move, transform, and trust their data — from the moment data moves, through every transformation, to the context teams and AI systems rely on. the company helps organizations automate data movement across the systems, clouds, engines, and tools they rely on. dbt Labs pioneered analytics engineering, helping teams transform data into reliable, governed insights. Together, we support thousands of organizations as they build a trusted foundation for analytics, AI, and better business decisions. As we bring our teams and technology together, we’re building on the strengths of both companies while continuing to deliver the products and experiences our customers know and trust. It’s an exciting time to join us: we’re creating a company with the scale, talent, and technology to help more organizations put their data to work with greater speed, confidence, and impact. During this transition period, you may see references to both the company and dbt Labs throughout our recruiting process as we integrate our teams, systems, and career sites.

About the Role

the company + dbt Labs is building data pipelines to power the modern data stack for thousands of companies. We’re looking for a Senior Staff Systems Engineer, Data and Integrations to join our Systems Engineering team and set the technical direction for the data that runs the company's business. In this role, you will own the data architecture behind our marketing, sales, billing, and product operations: the canonical data models, the contracts between systems, and the pipelines and transformations that move data reliably between them. You will lead our largest and most consequential data initiatives, such as system migrations, org consolidations, and re-platforming of core integrations, from design through validation and cutover.

This position sits at the crossroads of data engineering, data modeling, and business process. You will partner closely with our Systems, Analytics, and Engineering teams, as well as Marketing, Revenue Operations, Accounting, Product Operations, and other business partner teams, and you will mentor the engineers who build within the standards you set.

This is a full-time position based out of our Oakland, California office.

Technologies You’ll Use

Our core systems are Salesforce, Netsuite, Coupa, Workday, and Zendesk, with BigQuery as our data warehouse. Our current integration stack includes Celigo and the company itself. You should be an expert in SQL and strong in a general purpose programming language such as Python or TypeScript, and comfortable evaluating and adopting new tools when necessary.

A data engineering background is required, with expertise in data modeling, ETL/ELT development, data quality testing, and data validation at scale. Experience with dbt or a similar transformation framework, and with API-based and event-driven integration patterns, is preferred. Help us bring established data engineering practices to internal business systems, and define what those practices should be here.

What You’ll Do

Data Architecture and Standards

• Own the canonical data models for the company's marketing, sales, billing, and product operations integrations, and define how business concepts map to data across our core systems.

• Define team-wide standards for data modeling, data quality, testing, governance, and documentation, and drive their adoption across Systems Engineering and partner teams.

• Contribute to the design of integration architecture, establishing guidance for when to use batch, real-time, and event-driven data movement.

Delivery of Complex Initiatives

• Lead the design and execution of our most complex data initiatives, including bulk migrations, system consolidations, and re-implementation of core integrations, from target-state design through validation, cutover, and stabilization.

• Build, review, and ship the SQL and Python transformations and pipelines behind these initiatives, setting the bar for code quality on the team.

• Design the validation and reconciliation approach for migrations and pipeline changes, and sign off with stakeholders that record counts, values, and business rules reconcile.

Reliability and Observability

• Design the monitoring, alerting, and data quality framework that gives end-to-end visibility into data freshness and accuracy across systems, and define the KPIs used to measure integration and data health.