Senior AI Data & Analytics Engineer II
New
S
SamsaraData analytics
This role is open to candidates residing in the US.Full-TimeSenior
SalaryAnnual Base Salary $137,445 — $231,000 USD
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Job Details
- Experience
- 8+ years of experience in data engineering, analytics engineering, or AI engineering
- Required Skills
- PythonSQLData modelingdbtDatabricks
Requirements
- Have 8+ years of experience in data engineering, analytics engineering, or AI engineering, including deep hands-on data engineering experience.
- Bring expert-level Python and SQL skills and strong hands-on data modeling and dbt experience.
- Have deep experience with data warehouse and lakehouse architectures, ETL/ELT, and the modern data stack.
- Have hands-on experience with Databricks or a similar platform, such as Snowflake or BigQuery.
- Have experience building semantic layers and structured documentation for AI ingestion and agentic workflows.
- Use agentic coding tools such as Claude Code or Cursor regularly, and verify and take ownership of AI-generated output.
- Have experience building, shipping, evaluating, and monitoring LLM or agent systems in production.
- Have led complex cross-team data initiatives and gathered requirements independently from technical and non-technical stakeholders.
- Have experience supporting Marketing, Sales, or Product teams.
Responsibilities
- Partner on the strategy and multi-quarter roadmap for the Marketing Data and Analytics data and AI foundation.
- Architect and own marketing ETL/ELT pipelines, Databricks Gold Layer tables, and core data models.
- Design semantic layers, data dictionaries, and structured documentation for self-serve BI and conversational analytics.
- Translate business questions from Marketing, Sales, and R&D into requirements and scalable solutions.
- Lead cross-team initiatives, establish pipelines and integrations, manage risks, and own reliability and data quality.
- Raise engineering quality through technical leadership and code review, remove bottlenecks, and mentor engineers and analysts.
- Build AI-enabled data products and agents, including integrations, retrieval pipelines, and evaluation practices.
- Identify and automate manual workflows across Marketing and GTM teams, from prototype through production.
- Write and review Python and SQL, verifying AI-generated code before production.
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