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# Data Operations Engineer - Minneapolis, MN

- **Company:** Datasite LLC
- **Location:** Minneapolis, MN, United States
- **Salary:** $99,000.0 - $172,700.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Big Data, Data Architecture, Information Engineering, Data Infrastructure, Data Integrity, Data Profiling, Data Systems, Software Debugging, Electronic Data Interchange (EDI), Standard Sql, DataOps, Data Streaming, User-Centered Design, Delivery Pipeline, Snowflake, Data Lineage, Low Latency, Data Pipelines
- **Published:** June 6, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=e199f216b1a4aa8b

## About the Role

Do you have experience in Tooling?, * Strong experience designing and operating data pipelines with defined latency, freshness, and accuracy SLAs

 * Expert SQL skills and proven ability to work with large, complex datasets across diverse partner schemas
 * Hands-on experience with modern data tooling such as Snowflake, dbt, Airflow, and schema registries
 * Practical, in-the-workflow use of agentic tooling to accelerate schema mapping, anomaly detection, data profiling, and pipeline debugging
 * Track record of building monitoring, alerting, runbooks, and reconciliation processes for systems with external commitments
 * Ability to ramp quickly on new partner ecosystems, data formats, and domains
 * Proven success leading work in ambiguous, fast-moving environments
 * Excellent collaboration, communication, and cross-team influence

Work Location & Flexibility

 * This role follows a hybrid work model and is open to candidates based near our Minneapolis office. Employees are expected to work on-site a minimum of two days per week.

## Description

As a Data Operations Engineer at Datasite, you own the full lifecycle of partner data as it moves through our systems - ingestion, transformation, validation, and reconciliation - bringing the monitoring and SLA discipline that sophisticated partners expect. You balance partner trust, engineering velocity, and long-term data platform health while enabling intelligent, contract-driven data exchange across our partner ecosystem.

You bring hands-on experience with modern data tooling (Snowflake, dbt, Airflow, schema registries) paired with practical, AI-augmented workflows that compress manual investigation into minutes. You will help ensure new partnerships are delivered on a foundation of trustworthy data, with the rigor and creative problem solving that lets the broader engineering team stop firefighting and start building.

How We Work Together

Strategic Data Leadership

 * Guide data architecture decisions that incorporate AI-augmented capabilities into ingestion, transformation, and reconciliation workflows for partner integrations.
 * Partner with Product, Engineering, and partner teams to develop flexible data roadmaps aligned to Datasite strategy while adapting to fast-evolving partner data needs.
 * Drive pipeline improvements that scale across diverse partner data formats, reduce operational overhead, and improve reliability of SLA-bound data products.
 * Maintain adaptable data contracts and schema strategies, enabling rapid onboarding of new partners in uncertain, high-velocity environments.

Cross-Team Collaboration & Influence

 * Identify and drive cross-platform improvements (schema registries, validation tooling, data contracts, lineage tracking) that enhance partner and developer experiences.
 * Collaborate across Engineering, Product, and partner teams to deliver AI-first, integration-ready data solutions.
 * Communicate complex data concepts clearly, translating pipeline design trade-offs and SLA commitments for diverse stakeholders.
 * Provide technical guidance that ensures alignment, simplicity, and consistency across data flows and partner integrations.

Problem Solving & Overcoming Obstacles

 * Evaluate trade-offs across freshness, accuracy, latency, and cost, especially in partner-driven and AI-augmented data workflows.
 * Simplify pipelines and drive down data debt while supporting rapid experimentation and onboarding of new partners.
 * Own ambiguous data challenges - mismatched schemas, silent failures, partial loads, reconciliation gaps - and drive them to resolution.
 * Apply strong diagnostics to identify root causes of data discrepancies and deliver resilient, auditable solutions.

Mentorship & Growth

 * Mentor engineers and analytics contributors through coaching and feedback, including adoption of modern and AI-augmented data practices.
 * Support team growth by promoting continuous learning, experimentation, and adaptability in data engineering methods.
 * Foster a culture of psychological safety, collaboration, and shared ownership of data quality.
 * Help raise the bar in hiring, ensuring alignment with Datasite's technical and cultural expectations.

Ownership & Accountability

 * Own end-to-end design and delivery of ingestion pipelines, transformation layers, reconciliation processes, and partner-facing data products.
 * Build pipelines with strong observability, alerting, and self-healing characteristics - so issues are identified and, where possible, remediated before they become partner-visible.
 * Track progress, manage risk, and adapt plans while maintaining a bias for action and high-quality execution.
 * Ensure new partnerships are delivered with care, reliability, and ingenuity, balancing speed with long-term data integrity.


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