Lead Data Engineer
New
J
JobgetherData analytics
Based in the United StatesFull-TimeLead
SalaryStarting annual base salary: $155,000–$175,000 USD
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Job Details
- Experience
- At least 7 years of experience in data engineering or analytics engineering
- Required Skills
- PythonSQLSalesforceSnowflakeData engineeringdbt
Requirements
- Have at least 7 years of experience in data engineering or analytics engineering.
- Have experience as a lead or senior technical owner of a production data platform.
- Bring extensive hands-on experience with Snowflake.
- Demonstrate strong proficiency in dbt and advanced SQL, with working knowledge of Python.
- Have experience integrating SaaS platforms and marketing APIs, including advertising platforms, GA4, Salesforce, and HubSpot.
- Be able to shape technical strategy, influence architecture decisions, and establish platform direction.
- Be able to diagnose complex pipeline issues, resolve root causes, and document solutions.
- Collaborate effectively with technical and non-technical stakeholders and communicate technical concepts clearly.
- Work independently, exercise sound judgment, and navigate ambiguity.
- Preferred: experience with agency data or B2B marketing analytics.
- Preferred: knowledge of data governance and privacy practices, including role-based access controls, data masking, and consent management.
- Preferred: experience preparing data for AI- and LLM-driven tools or mentoring colleagues and leading technical teams.
Responsibilities
- Build, maintain, test, and document data pipelines and dbt models in Snowflake.
- Integrate paid media, web analytics, SEO, CRM, and other business application data using ELT platforms and direct API integrations.
- Monitor pipelines, investigate failures, resolve root causes, and prevent disruptions.
- Maintain data governance standards, including access controls, privacy safeguards, and consent requirements.
- Develop semantic layers, consistent metric definitions, and metadata for reliable AI interpretation of data.
- Design secure, governed access patterns for AI tools, including scoped, read-only queries and traceable results.
- Partner with senior leadership to define data architecture, technical roadmaps, and build-versus-buy-versus-partner decisions.
- Translate business and client requirements into scalable data solutions and provide technical guidance.
- Build and lead a data support team, establishing triage practices, playbooks, escalation procedures, and workflows.
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