Senior Analytics Engineer, AI & DX Analytics

Block•New York, NY

18h•$139,000 - $245,400•Remote

About The Position

The AI & DX (Developer Experience) 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. Your work will be the trusted source of truth that engineers and executives use to decide where AI investment is working and where to redirect it. We're hiring a Senior Analytics Engineer to own parts of that pipeline end to end. This is a broad, high-visibility role with a lot of white space: you will instrument new telemetry at the source and build production pipelines that turn it into governed data, then turn that governed data into executive-facing reporting and the analysis that surfaces concrete opportunities to improve AI ROI and developer experience. You'll also help pioneer one of the most consequential open problems in the industry right now: measuring the productivity impact of an increasingly AI- and agent-augmented engineering workforce, where there is no textbook answer and real room to set the standard. 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.

Requirements

Self-sufficient across the full analytics loop: instrumentation, pipeline building, metric definition, and dashboards — not someone who hands off past the query.
At home in messy, evolving telemetry, with the judgment to know when a new number is right and when it needs a second look before an executive sees it.
A strong product thinker: you get at what a stakeholder actually needs from a metric or chart, not just what they asked for.
Comfortable with ambiguity on a genuinely unsolved problem — measuring AI/agent productivity impact — and motivated to go set the standard rather than wait for one.
An exceptional communicator, able to defend a metric's methodology and a chart's framing directly to senior stakeholders.
Thoughtful about using AI coding agents in your own craft: fast to build with them, and rigorous about evaluating what they produce rather than taking it on faith.
8+ years in analytics engineering, data engineering, or business intelligence, owning production data pipelines and insights end to end.
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 instrument new event or telemetry data, not only transforming what's already captured.
Experience building dashboards or internal tools that non-technical stakeholders rely on to make decisions.
Comfort with git, CI/CD systems, and debugging production pipeline failures such as retries and backfills.

Nice To Haves

Experience with a metrics-governance or semantic-layer system, such as a metrics store, dbt semantic layer, or LookML.
SQL (Snowflake), Python, TypeScript and React, Git, CI/CD pipelines, a governed metrics store, AI coding agents, and LLM-based classification tooling.

Responsibilities

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.
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.
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.
Push the industry-frontier work of quantifying AI and agent productivity impact — there's no established playbook, and this team sets one.