Lead Data Engineer, Databricks (Remote, US)
V4C.ai · Remoto · Remote
Recebendo candidaturas · conferida em 14/11
Ficha da vaga
- Modelo
- Remoto
- Onde aceita
- Não informado
- Senioridade
- Liderança
- Salário
- Salário não divulgado
Fonte: Workable · Ver vaga original →
- python
- sql
- ci cd
- mentoring
Organizei a descrição em seções com IA. O texto completo da vaga está logo abaixo.
Sobre a vaga
V4C.ai is seeking a Lead Data Engineer with expertise in Databricks to lead production Lakehouse pipeline builds on customer engagements, working directly with clients and Databricks field teams.
O que você vai fazer
- Lead engineering on customer engagements, from design through production handover
- Build and optimize batch and streaming pipelines using PySpark, SQL, and Delta Lake on Databricks
- Implement Medallion architecture across cloud ecosystems and set the layer conventions the team follows
- Own performance and cost: benchmark Spark jobs, tune SQL, right-size compute, and explain the trade-offs to the customer
- Implement data quality and validation as code, including Lakeflow expectations and automated testing in CI
Descrição completa da vaga
About V4C V4C is a pure-play Databricks partner, and in under two years we have earned 850+ Databricks certifications across 325+ customers and been named Databricks' 2026 Partner of the Year (C&SI Emerging). As a Lead Data Engineer, you will lead the build of production Lakehouse pipelines on customer engagements, working directly with customers and often Databricks' own field teams. Every technical hire here gets a certification roadmap built for them and taught in-house by Databricks-approved trainers, plus a dedicated Employee Success Manager whose only responsibility is your experience at V4C.
The Role Lead Data Engineers are the technical backbone of V4C delivery. You lead the engineering workstream on an engagement — translating an architecture into working pipelines, setting the patterns the team follows, and owning the quality of what ships. You will work across batch and streaming, on messy enterprise source systems, under real deadlines, and in front of the customer explaining what you built and why. The difference from a Data Engineer is ownership: you are accountable for a workstream and the engineers on it. Responsibilities Lead engineering on customer engagements, from design through production handover Build and optimize batch and streaming pipelines using PySpark, SQL, and Delta Lake on Databricks Implement Medallion architecture across cloud ecosystems and set the layer conventions the team follows Own performance and cost: benchmark Spark jobs, tune SQL, right-size compute, and explain the trade-offs to the customer Implement data quality and validation as code, including Lakeflow expectations and automated testing in CI Mentor mid-level and junior engineers through code review, pairing, and design feedback Partner with the Data Architect on design decisions, and escalate when implementation reveals the design will not hold
Perguntas e respostas sobre a vaga
- Aceita candidatos de onde?
- A vaga não diz.
- Qual o regime de contratação?
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- É remota?
- Sim, é remota.
- Informa salário?
- Não, a vaga não divulga.
Nenhuma vaga séria cobra para você se candidatar, e eu nunca peço dado bancário dentro de uma candidatura. O plano pago é cobrado só pelo Asaas, meu parceiro de pagamento.
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