• Help coordinating day-to-day work with offshore vendor engineering resources — setting priorities, sequencing deliverables, and keeping their work aligned to sprint commitments and the platform roadmap.

• Translate business and technical requirements into clear specifications, acceptance criteria, and design guidance that offshore teams can execute with minimal ambiguity.

• Quality-check offshore deliverables through code review, testing, and validation against data standards, performance targets, and definition-of-done before changes are promoted to production.

• Collaborates with data architects, analysts, and business stakeholders to keep data accurate and well-governed, building alignment within the team and with immediate cross-functional partners on delivery.

• Uphold engineering standards, code review practices, and documentation conventions across both onshore and offshore contributors.

• Support the team's growth by training and coaching engineers on tools, standards, and best practices as the platform scales.

What Success Looks Like

• Reliable, well-modeled data products that stakeholders trust and use without rework or manual reconciliation.

• Pipelines that run efficiently and cost-effectively, with issues caught proactively through monitoring rather than reported by downstream users.

• Machine learning models moved from experimentation into governed production with reproducible, monitored MLOps workflows.

• Offshore and vendor deliverables consistently meet quality and standards on first review, with minimal rework.

• Recognized as a technical lead others rely on, able to represent the team, and unblock engineers.

How Impact Will be Measured

• Data quality, pipeline reliability, and freshness SLAs met across owned datasets.

• Reduction in data incidents and in time-to-resolution for pipeline and reconciliation issues.

• On-time delivery of dimensional models and data products that unblock analytics and AI initiatives.

What You’ll Bring to the Team

Experience That Matters Most

• 7+ years in data engineering on big data and cloud platforms, including 3+ years hands-on with Databricks (Spark/PySpark, Delta Lake, jobs).

• Proven delivery of Kimball / dimensional data models in a modern warehouse or lakehouse, with strong SQL and Python (PySpark).

• Production experience with dbt (models, tests, snapshots) and with Unity Catalog for governance, access control, and lineage.

• Working knowledge of the ML lifecycle and MLOps, including MLflow for experiment tracking, model registry, and deployment.

• Experience translating business and technical requirements into clear specifications and coordinating or overseeing offshore and vendor engineering resources, including reviewing their deliverables for quality.

• Strong communication and stakeholder skills, with a track record of mentoring engineers and setting technical standards.

Experience That’s Great to Have

• Real-time / streaming experience (Structured Streaming, Kafka, or Azure Event Hubs) and familiarity with the Salesforce data model.

• Cost governance across multi-workspace Databricks environments — cluster policies, tagging, and system.billing.usage analysis.

• BI / visualization exposure (Power BI, Tableau, or Databricks dashboards/Genie) and containerization (Docker) for reproducible workflows.

• Prior technical-lead, team-lead, or technical-management exposure.

• Experience managing vendor or partner relationships, or distributed and offshore delivery models.

Risepoint is an equal-opportunity employer and supports a diverse and inclusive workforce.

Originally posted on Himalayas