Analytics Engineer

• Writes and reads joins at scale, window functions, CTEs and incremental logic with confidence

• Can tell when a query result is wrong and explain why

• Builds clean, reusable tables and datamarts that are easy to extend, test and document

• Hands-on with dbt, Airflow or similar, plus Git and code reviews

• Has used SQL warehouses, notebooks and scheduled jobs, and understands catalog, schema, table and permissions (advanced Spark tuning not required)

• Uses tests, monitoring and alerts by default; comfortable with on-call and fixing issues at the root

• Builds scripts and API integrations that others can run without the author

• Uses Claude Code, Codex, Cursor or similar, not just autocomplete

• Writes a spec before building (inputs, outputs, transformations, checks), delegates multi-step tasks to the agent and reviews the result

• Can show a before and after

• Has built at least one agent or workflow that chains LLM calls with tools (SQL, APIs, MCP servers) and that other people actually use

• Understands failure modes, guardrails, and how to test an agent with real inputs

• Has caught AI work that looked right but was wrong (e.g., a plausible SQL query with a bad join) and added a check so it doesn’t happen again

• Knows when human review is required, especially for customer data sent to marketing tools

• Writes context and runbooks (READMEs, agent instructions, data catalog entries) that let a teammate or an AI agent maintain the system without the author

• Understands what Marketing, Growth and Marketplace need and decides whether the answer is a table, a pipeline, an agent or a self-service tool, instead of a one-off report

• Manages their own backlog and ships without close direction

• Communicates clearly with technical and non-technical people; English is a must

• Desirable Qualifications

  • Experience with marketing tools: CRM (Braze or similar), app attribution (AppsFlyer or similar), and tag management (GTM).

  • Reverse ETL or CDP experience.

  • Deeper data platform experience: advanced pipeline tools, Spark performance tuning, or a data platform certification.

  • Experience with LLM APIs, LiteLLM, or building MCP servers.

  • A/B testing and basic statistics.

  • Knowledge of data privacy rules (consent, opt-out, Habeas Data).

  • AWS and fintech or other regulated industry background.

We believe in a fast, transparent, and engaging hiring experience that allows both you and us to determine if there's a great fit. Here’s what our process looks like:

• Step 1: People Interview (30 min)
  A conversation with a recruiter or hiring manager to get to know you, your experience, and what you're looking for. We’ll also share more about Addi, our culture, and the role.

• Step 2: Initial Interview (45-60 min)
  A more in-depth conversation with the hiring manager, where we explore your skills, experience, and problem-solving approach. We want to understand how you think and work.

• Step 3: Take Home Challenge (5-6 days)
  Complete a simple take-home challenge within a 1-week window. With this technical challenge, we want to see your technical expertise solving a real-world problem. We expect that you invest 5 hours or less in developing a working solution.

• Step 4: Take Home Challenge Review (60 min)
  Meet with a Data Scientists and the Data Science Lead to talk about your take-home exercise submission and any questions you might have.

• Step 5: Stakeholder Interview: (30 min)
  Meet with cross-functional partner to talk about how you collaborate, communicate insights, and drive business impact, and to ask any questions about the team and its context.

• Step 6: Co-Founder Interview
  If there’s a strong match, you’ll have a final conversation with our Founder to align on expectations, cultural fit and ensure mutual excitement. From there, we’ll move quickly to an offer and discuss next steps.

We value efficiency and respect for your time, so we aim to complete the process as quickly as possible. Our goal is to make this experience insightful and exciting for you, just as much as it is for us. Regardless of the outcome, we are committed to always providing feedback, ensuring that you walk away with valuable insights from your experience with us.