Data Operations Engineer

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.

• What We're Looking For
• 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