Data Operations Engineer - Minneapolis, MN

## Data Operations Engineer - Minneapolis, MNApplyremote type:
Remote locations:
USA - MN - Minneapolis:
Remote USAtime type:
Full time posted on:
Posted Todayjob requisition :
R36195

Datasite and its associated businesses are the global center for facilitating economic value creation for companies across the globe. From data rooms to AI deal sourcing and more. Here you’ll find the finest technological pioneers:
Datasite, Blueflame AI, Grata, and Sherpany. They all, collectively, define the future for business growth. Apply for one position or as many as you like. Talent doesn’t always just go in one direction or fit in a single box. We’re happy to see whatever your superpower is and find the best place for it to flourish. Get started now, we look forward to meeting you..
*
* Job 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…