Staff / Lead Data Analyst / Analytics Engineer

We are hiring a Staff / Lead-level Data Analyst / Analytics Engineer to embed with the Monetization Data Science & Analytics team as a senior individual contributor and technical leader. This person will be the go-to analytics expert for advertiser revenue, monetization performance, and growth metrics — trusted by Data Scientists, Analysts, PMs, and Engineering leaders to drive high-impact work end-to-end.

This is a staff-level Individual Contributor role, not a mid-level execution seat. The successful candidate operates as:

• A trusted thought partner to Data Scientists and Product leaders — someone who improves the quality of the question before answering it.
• A technical leader who sets standards for data models, pipelines, and dashboards that others follow.
• A force multiplier who unblocks the team by identifying and fixing root causes across the data stack, not just building what's asked.

What You’ll Own:

• 50% — Analytics, Business Insights & Technical Leadership Partnering with Data Scientists and Product on the hardest analytics problems; driving metric definitions; reviewing others' analyses; setting standards for the team's analytics work.
• 30% — Data Engineering & Pipeline Ownership Architecting and owning production SQL pipelines, data models, and data cubes; designing and operating Airflow DAGs; setting the bar for data quality, reliability, and reconciliation across the domain.
• 20% — Data Visualization, Metric Governance & Enablement Owning executive-visibility dashboards in Tableau / Superset; defining and governing metrics; enabling self-serve analytics for the broader Monetization org.

Analytics Leadership & Business Partnership

• Serve as the senior analytics IC for the Monetization Analytics pod — the person Data Scientists and PMs come to with the hardest, most ambiguous data problems.
• Improve the quality of the question before answering — reframe vague asks into sharper, more valuable analytical approaches.
• Lead end-to-end analytics initiatives that span data modeling, pipeline work, and dashboard delivery — with minimal supervision and clear stakeholder communication throughout.
• Set metric definitions and standards for advertiser revenue, monetization performance, funnel/cohort metrics, and experiment readouts — and drive consistency across dashboards.
• Independently drive root-cause analysis on data discrepancies across dashboards, warehouses, or pipelines — including cross-team debugging when needed.
• Review, coach, and raise the bar on the work of other analysts and analytics engineers on the team.

Data Engineering & Pipeline Architecture

• Architect and own production-grade SQL data pipelines ( Presto / Trino / Hive / Spark SQL ) — including making the right tradeoffs on incremental vs. full refresh, pre-aggregation, and cost/performance.
• Design and own data cubes, aggregate tables, and semantic layers used by the whole Monetization Analytics function.
• Author, own, and operate Airflow DAGs for critical revenue and monetization pipelines — including SLAs, on-call posture, backfills, and incident response.
• Set and enforce standards for data quality, reconciliation, and observability — row counts, revenue tie-outs, distribution checks, anomaly alerting — across the domain.
• Optimize existing pipelines aggressively for cost and latency (partitioning, incremental refresh, query tuning on billion+ row tables) — and quantify the wins.
• Contribute to cross-team technical decisions — table designs, upstream schema changes, migration plans (e.g., Hive → Trino) — via design docs and reviews.

Data Visualization, Metric Governance & Enablement

• Own the design and quality of executive and cross-functional dashboards in Tableau and/or Superset .
• Drive metric governance — clear definitions, owners, source-of-truth queries, validation, deprecation.
• Enable self-serve analytics for Data Scientists, Analysts, and PMs — clear naming, documentation, certified metrics, sensible defaults, and coaching.

You’ll Thrive in This Role If You Have:

• 9+ years of combined experience in Data Analytics, Business Intelligence, or Analytics Engineering roles.
• At least 3+ years at Senior level or above in an analytics-adjacent role at a high-scale tech, ads-tech, marketplace, or fintech company.
• Prior experience as the most senior analytics IC on an embedded team — or a strong case for why they're ready to step into that role now.
• Track record of leading end-to-end analytics initiatives — from ambiguous business question through data model, pipeline, dashboard, and rollout.
• Prior experience partnering directly with US-based Data Science, Product, and Engineering leaders as a full contributor.

Technical Skills — SQL & Data Engineering (Advanced)

• Expert-level SQL — deep proficiency with window functions, CTEs, complex joins, query optimization, incremental patterns, skew mitigation, and cost tuning on billion+ row tables.
• Deep hands-on with at least two of: Presto, Trino, Hive, Spark SQL, Snowflake, BigQuery, Redshift . Can reason about query plans and physical layout, not just syntax.
• Advanced Airflow — has architected and operated large DAG ecosystems (50+ production DAGs), including cross-DAG dependencies, backfills at scale, and SLA management. Equivalent orchestrators (Dagster, Prefect) also acceptable if depth is comparable.
• Data architecture & modeling depth — Kimball, star schema, dimensional modeling, OLAP cubes, wide fact tables, slowly-changing dimensions, semantic layer design. Can defend design tradeoffs in a design review.
• ETL / ELT architecture — incremental loads, backfills, idempotency, data quality frameworks, lineage.
• Python for data work — pandas, PySpark, scripting, and light tooling development.
• dbt or equivalent transformation framework experience strongly preferred.
• Experience contributing to or reviewing design docs and RFCs for data platforms and pipelines.

Technical Skills — Visualization

• Deep, hands-on production experience building executive-grade dashboards in Tableau and/or Apache Superset (Looker, Power BI, Mode also acceptable).
• Strong opinions on dashboard design — headline vs. drilldown metrics, layout, filters, performance, self-serve UX.
• Experience driving metric governance and self-serve BI at an org level.

Analytics & Business Skills

• Strong grasp of KPI definition, metric design, funnel analysis, cohort analysis, and A/B testing methodology.
• Deep exposure to digital advertising / monetization metrics — impressions, clicks, CTR, CPM, CPC, CVR, ROAS, revenue attribution, incrementality — is strongly preferred.
• Prior experience at ads-tech, digital media, or major consumer/marketplace tech companies (Meta, Google, Amazon, Uber, DoorDash, Snap, TikTok, LinkedIn, Airbnb, Instacart, Pinterest peers, etc.) is a strong plus.
• Comfort reading experiment results and challenging methodology when needed.

Leadership, Communication & Ways of Working

• Native or near-native English (spoken and written) — this is a hard requirement.
• Track record of leading initiatives end-to-end with minimal direction — scoping, aligning stakeholders, executing, and communicating results.
• Comfortable pushing back on unclear or misdirected requirements and proposing better approaches.
• Prolific writer of design docs, RFCs, requirement docs, and postmortems.
• Experience mentoring or coaching less-senior analysts and analytics engineers — even if not a formal manager.
• Executive presence — can present analytics work to Director/VP-level stakeholders and defend recommendations.
• Operates with the ownership mindset of a permanent employee, even in a contract role.