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, with 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 optimized 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 a track record of leading complex, cross-team data initiatives and independently gathering requirements from technical and non-technical stakeholders.
- Have experience supporting Marketing, Sales, or Product teams.
Responsibilities
- Partner on defining the strategy and multi-quarter roadmap for MDA's data and AI foundation.
- Architect and own marketing data infrastructure, including 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 ambiguous business questions from Marketing, Sales, and R&D into requirements and scalable solutions.
- Lead cross-team initiatives end to end, including new data pipelines and integrations, and own reliability and data quality.
- Lead technical work on the team's hardest problems, improve engineering quality 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 with AI across Marketing and GTM teams, from concept through production.
- Ship Python and SQL, direct agentic coding tools, and review and verify AI-generated work.
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