Data Operations Engineer

Datasite is a global center for facilitating economic value creation for companies. The Data Operations Engineer will own the full lifecycle of partner data, ensuring reliable data exchange and collaboration across teams while enhancing data architecture and workflows.

Responsibilities:

• 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
• 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
• 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
• 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
• 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

Requirements:

• 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