Academic Partnerships
Senior Data Engineer
Vaga remota de Data Engineer com fit claro de localização do candidato.
Publicada25 de jul. de 2026
Países elegíveis1 país aceito
Sinal de senioridadeSenior
Modelo de trabalhoRemoto
Locais aceitos para candidatos
Estados Unidos
Resumo da vaga
Senior Data Engineer
Requisitos e responsabilidades
Conteúdo da vaga extraído em seções para revisão mais rápida.
What You Will Do
- 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.
What You Will Do
- Ingest data from operational and SaaS sources such as Salesforce into the lakehouse, favoring managed connectors like Lakeflow Connect where appropriate.
What You Will Do
- Build and maintain Kimball-style dimensional models — facts, conformed dimensions, and slowly changing dimensions — as the analytics layer of record.
What You Will Do
- Develop, test, and document transformations in dbt (models, sources, snapshots, tests, exposures) with strong CI discipline.
What You Will Do
- Manage data assets in Unity Catalog, including catalogs, schemas, permissions, and lineage.
What You Will Do
- Optimize performance and cost through cluster and warehouse sizing, Spark tuning, partitioning, and tagging for cost attribution.
What You Will Do
- Operationalize machine learning workflows using MLflow for experiment tracking, model registry, and deployment, applying MLOps best practices.
What You Will Do
- 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.
What You Will Do
- Translate business and technical requirements into clear specifications, acceptance criteria, and design guidance that offshore teams can execute with minimal ambiguity.
What You Will Do
- 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.
What You Will Do
- 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.
What You Will Do
- Uphold engineering standards, code review practices, and documentation conventions across both onshore and offshore contributors.
What You Will Do
- 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.
What Success Looks Like
- Pipelines that run efficiently and cost-effectively, with issues caught proactively through monitoring rather than reported by downstream users.
What Success Looks Like
- Machine learning models moved from experimentation into governed production with reproducible, monitored MLOps workflows.
What Success Looks Like
- Offshore and vendor deliverables consistently meet quality and standards on first review, with minimal rework.
What Success Looks Like
- 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.
How Impact Will be Measured
- Reduction in data incidents and in time-to-resolution for pipeline and reconciliation issues.
How Impact Will be Measured
- On-time delivery of dimensional models and data products that unblock analytics and AI initiatives.
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).
Experience That Matters Most
- Proven delivery of Kimball / dimensional data models in a modern warehouse or lakehouse, with strong SQL and Python (PySpark).
Experience That Matters Most
- Production experience with dbt (models, tests, snapshots) and with Unity Catalog for governance, access control, and lineage.
Experience That Matters Most
- Working knowledge of the ML lifecycle and MLOps, including MLflow for experiment tracking, model registry, and deployment.
Experience That Matters Most
- Experience translating business and technical requirements into clear specifications and coordinating or overseeing offshore and vendor engineering resources, including reviewing their deliverables for quality.
Experience That Matters Most
- 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.
Experience That’s Great to Have
- Cost governance across multi-workspace Databricks environments — cluster policies, tagging, and system.billing.usage analysis.
Experience That’s Great to Have
- BI / visualization exposure (Power BI, Tableau, or Databricks dashboards/Genie) and containerization (Docker) for reproducible workflows.
Experience That’s Great to Have
- Prior technical-lead, team-lead, or technical-management exposure.
Experience That’s Great to Have
- Experience managing vendor or partner relationships, or distributed and offshore delivery models.
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