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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and building scalable data pipelines and dimensional models using Databricks, PySpark, and SQL, while ensuring data governance and quality. Proficient in operationalizing machine learning workflows and mentoring engineering teams to uphold technical standards and best practices.
Highest-signal resume keywords
DatabricksPySparkSQLDbtMLOps
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data EngineeringDimensional ModelingKimball MethodologyData Pipeline DevelopmentMachine Learning Workflows
Soft Skills
Strong CommunicationStakeholder ManagementMentoring
Tools & Technologies
Unity CatalogMLflowDelta LakeLakeflow Connect
Industry Keywords
Big DataCloud PlatformsData GovernanceData Quality
Tech Stack
Tools & technologiesCloudPySparkPythonSparkSQLUnity
About the role
Key responsibilities & impact- Design, build, and own scalable data pipelines and dimensional models on Databricks (PySpark, SQL, medallion architecture) — delivering trusted, on-time data products and meeting SLAs within your assigned scope.
- Ingest data from operational and SaaS sources such as Salesforce into the lakehouse, favoring managed connectors like Lakeflow Connect where appropriate.
- Build and maintain Kimball-style dimensional models — facts, conformed dimensions, and slowly changing dimensions — as the analytics layer of record.
- Develop, test, and document transformations in dbt (models, sources, snapshots, tests, exposures) with strong CI discipline.
- Manage data assets in Unity Catalog, including catalogs, schemas, permissions, and lineage.
- Optimize performance and cost through cluster and warehouse sizing, Spark tuning, partitioning, and tagging for cost attribution.
- Operationalize machine learning workflows using MLflow for experiment tracking, model registry, and deployment, applying MLOps best practices.
- 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.
Requirements
What you’ll need- 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.
Benefits
Comp & perks- Risepoint is an equal-opportunity employer and supports a diverse and inclusive workforce.
