Senior Analytics Engineer, AI & DX Analytics

New York, NY · US - CA - Bay Area - Remote · 8+ years experience · Remote · Posted Sep 24, 2026

At a glance

The AI & DX Analytics team measures and improves Block's developer experience and investment in AI by turning raw engineering signals into reporting and analysis.
As a Senior Analytics Engineer, you will own parts of the pipeline end to end, instrumenting new telemetry at the source and building production pipelines that turn it into governed data.
You will help pioneer measuring the productivity impact of an increasingly AI- and agent-augmented engineering workforce by defining new metrics and crafting executive-facing visual narratives.

Instrument and extend the raw telemetry captured about AI usage, code changes, CI/CD activity, and spend.
Partner with data engineers on production ETL pipelines that land telemetry into governed tables with freshness monitoring, backfills, and alerting.
Define new metrics out of ambiguous or evolving signals and earn stakeholder trust by validating and reconciling them.
Build and ship executive-facing dashboards and visualizations that turn governed metrics into decision-ready reporting.
Run analysis needed for new charts or metrics, anticipate executive questions, and bring senior stakeholders recommendations with defended methodology.
Translate complex, technical findings into a clear narrative with a headline chart and a concise summarizing sentence.
Use AI coding agents as a default part of writing, testing, and maintaining data pipelines, dashboards, and analyses.
Partner across data engineering, applied AI, and data science teams to keep telemetry connected end to end.
Turn data insights into action by flagging where AI spend isn't paying off or developer friction is costly, pushing for tooling, process, or investment changes.
Push industry-frontier work of quantifying AI and agent productivity impact to set a new playbook.

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 is 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.
Self-sufficient across the full analytics loop: instrumentation, pipeline building, metric definition, and dashboards.
At home in messy, evolving telemetry with the judgment to know when a new number is right.
A strong product thinker who understands what stakeholders actually need from a metric or chart.
Comfortable with ambiguity on the unsolved problem of measuring AI/agent productivity impact.
Exceptional communicator able to defend a metric's methodology and chart framing directly to senior stakeholders.