Requirements
Must have:
- Strong experience designing and running data pipelines with defined latency, freshness, and accuracy SLAs - Advanced SQL skills and proven ability to work with large, complex datasets across varied partner schemas - Hands-on experience with modern data tools such as Snowflake, dbt, Airflow, and schema registries - Practical use of agentic tooling within workflows to speed up schema mapping, anomaly detection, data profiling, and pipeline troubleshooting - Demonstrated success building monitoring, alerting, runbooks, and reconciliation processes for externally committed systems - Ability to ramp up quickly on new partner ecosystems, data formats, and domains - Proven ability to lead work in ambiguous, fast-paced environments - Excellent collaboration, communication, and cross-functional influence skills
Responsibilities:
- Lead data architecture decisions that incorporate AI-augmented capabilities into ingestion, transformation, and reconciliation workflows for partner integrations - Partner with Product, Engineering, and external partner teams to shape flexible data roadmaps aligned with our strategy and evolving partner needs - Improve pipelines so they scale across diverse partner data formats, reduce operational overhead, and strengthen reliability of SLA-bound data products - Maintain adaptable data contracts and schema strategies to enable rapid onboarding of new partners in uncertain, high-velocity environments - Drive cross-platform improvements such as schema registries, validation tooling, data contracts, and lineage tracking to 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 and explain pipeline trade-offs and SLA commitments to varied stakeholders - Provide technical direction to ensure alignment, simplicity, and consistency across data flows and partner integrations - Assess trade-offs across freshness, accuracy, latency, and cost in partner-driven and AI-augmented data workflows - Simplify pipelines, reduce data debt, and support rapid experimentation and onboarding of new partners - Own ambiguous data issues such as mismatched schemas, silent failures, partial loads, and reconciliation gaps, and drive them to resolution - Use strong diagnostics to identify root causes of data discrepancies and deliver resilient, auditable solutions - Mentor engineers and analytics contributors through coaching and feedback, including adoption of modern and AI-augmented data practices - Support team growth by encouraging 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 by ensuring alignment with our technical and cultural expectations - 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 capabilities so issues are detected and, where possible, remediated before reaching partners - Track progress, manage risk, and adjust plans while maintaining a bias for action and high-quality execution - Ensure new partnerships are delivered with care, reliability, and ingenuity while balancing speed with long-term data integrity
Company:
We are Datasite, part of a group that also includes Blueflame AI, Grata, and Sherpany, and we operate at the global center of economic value creation for companies worldwide, from data rooms to AI deal sourcing and beyond. We are hiring a Data Operations Engineer for a hybrid role based near our Minneapolis office, with on-site work expected at least two days per week.
We offer a competitive compensation package with a base salary range of $99,000 to $172,700, and benefits including medical, dental, and vision insurance, a retirement savings plan, paid time off, and other employee benefits. We are committed to building a diverse, inclusive, and equitable workplace where every team member is respected and valued.
