Senior Analytics Engineer, AI & DX Analytics

About Us

Block builds simple, powerful tools that make progress towards an economy that's truly open to all. Each of our brands unlocks different aspects of the economy for more people. Square makes commerce and financial services accessible to sellers. Cash App is the easy way to spend, send, and store money. Afterpay is transforming the way customers manage their spending over time. TIDAL is a music platform that empowers artists to thrive as entrepreneurs. Bitkey is a simple self-custody wallet built for bitcoin. Proto is a suite of bitcoin mining products and services. Together, we're helping build a financial system that is open to everyone.

The Role

The AI & DX Analytics team exists to measure and improve Block's developer experience and investment in AI. We turn raw engineering signals (agent session data, pull request and CI activity, and AI spend) into reporting and analysis that shape leadership's AI investment decisions, and drive improvement in AI ROI and developer experience.

We're hiring a Senior Analytics Engineer to own parts of that pipeline end to end. This role drives two key parts: You will instrument new telemetry at the source and build production pipelines that turn it into governed data. You will then turn that governed data into executive-facing reporting that leadership will use to decide where AI investment is working and where to redirect it, and into the analysis that surfaces concrete opportunities to improve AI ROI and developer experience. You'll define new metrics and craft the visual narrative — the charts, dashboards, and presentation materials — that make them land with an executive audience. AI-first workflows are central to how this team operates and critical to succeeding in this role: using AI coding agents is a default part of how you build and maintain pipelines and dashboards. You'll excel in this role if you're as comfortable defining and visualizing a brand new executive-facing metric from a messy signal as you are debugging why a pipeline silently stalled overnight.

Responsibilities

Instrumentation & Telemetry

• Instrument and extend the raw telemetry captured about AI usage, code changes, CI/CD activity, and spend — building new data capture, not only transforming what already exists. This is core to the role, as evolving AI tools and increased AI adoption constantly creates new signals that need to be captured.
• Partner with data engineers on production ETL pipelines that land that telemetry into governed tables, with the freshness monitoring, backfills, and alerting that keep it trustworthy, rather than a one-off script.
• Define new metrics out of ambiguous or evolving signals, and earn stakeholder trust in a new number by validating and reconciling it until it holds up.

Dashboards & Analysis

• Build and ship the executive-facing dashboards and visualizations that turn governed metrics into decision-ready reporting — with the polish and precision a room of executives will scrutinize.
• Run the analysis that a new chart or metric needs and anticipate questions that an executive audience will ask, then bring senior stakeholders a recommendation, not just a number, and defend the methodology behind both.
• Translate complex, technical findings into a clear narrative: the headline chart and the one sentence that makes an executive act on it.

Across Both Areas

• Use AI coding agents as a default part of how you write, test, and maintain data pipelines, dashboards, and analyses.
• Partner across data engineering, applied AI, and data science teams to keep telemetry connected end to end — the raw signal you capture directly feeds the session classifiers, not just the dashboards you build.
• Turn what the data shows into action: flag where AI spend isn't paying off or where developer friction is most costly, and push the tooling, process, or investment changes that address it.

Requirements

• 8+ years in analytics engineering, data engineering, or business intelligence, with a track record of owning production data pipelines and insights end to end, not just writing ad hoc queries.
• Background in developer experience, engineering productivity, measuring AI effectiveness, or platform analytics.
• Strong SQL and Python, with hands-on experience building and operating data pipelines against a cloud data warehouse such as Snowflake.
• Experience partnering with engineering teams to define and instrument new event or telemetry data, not only transforming data that is already captured.
• Experience building dashboards or internal tools that non-technical stakeholders rely on to make decisions, and the judgment to know when something is decision-ready versus still an analysis.
• Comfortable owning a dashboard's performance and data layer, not just its charts — able to profile a slow query, rework a caching or prequery layer, and ship the frontend changes yourself in a framework like React; we'll help you ramp up if this is new.
• Comfort with git, CI/CD systems, and debugging production pipeline failures such as retries and backfills.
• Workflows built around AI coding agents as a primary tool for writing and maintaining code.
• Strong written and verbal communication, with experience presenting technical findings and methodology directly to senior stakeholders.
• A track record of producing polished charts and materials for executive audiences — comfortable sweating details like labeling, framing, and precision.
• Demonstrated ability to lead cross-team collaboration.

Nice to Have

• Experience with a metrics-governance or semantic-layer system, such as a metrics store, dbt semantic layer, or LookML.

Technologies

SQL (Snowflake), Python, TypeScript and React, Git, CI/CD pipelines, a governed metrics store, AI coding agents, and LLM-based classification tooling.