Lead Analytics Engineer

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

Requirements:

• 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