Note: The job is a remote job and is open to candidates in USA. Innodata is a global data engineering company focused on enabling the responsible advancement of artificial intelligence through data, evaluation frameworks, platforms, services, and human expertise. The Lead Analytics Engineer will serve as a staff-level individual contributor and technical leader for the Monetization Data Science & Analytics team, owning analytics initiatives, production data pipelines, metric governance, and executive dashboards while partnering with Data Scientists, Product leaders, and Engineering teams.
Responsibilities
- **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
- 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
- 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
- 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
Skills
- 9+ years of combined experience
- 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
- 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
- Experience contributing to or reviewing design docs and RFCs for data platforms and pipelines
- 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
- Strong grasp of KPI definition, metric design, funnel analysis, cohort analysis, and A/B testing methodology
- Comfort reading experiment results and challenging methodology when needed
- 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
- Dbt or equivalent transformation framework experience strongly preferred
- 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
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