Lead Analytics Engineer

Innodata Inc.

5d•$130,000 - $150,000

About The Position

Innodata is seeking a Staff / Lead-level Data Analyst / Analytics Engineer to join the Monetization Data Science & Analytics team. This role is a senior individual contributor and technical leader responsible for advertiser revenue, monetization performance, and growth metrics. The ideal candidate will act as a trusted thought partner, technical leader, and force multiplier, driving high-impact work end-to-end. The role involves partnering with Data Scientists and Product leaders, setting standards for data models and pipelines, and identifying and fixing root causes across the data stack.

Requirements

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 readiness to step into that role.
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 proficiency, including window functions, CTEs, complex joins, query optimization, incremental patterns, skew mitigation, and cost tuning on billion+ row tables.
Deep hands-on experience with at least two of: Presto, Trino, Hive, Spark SQL, Snowflake, BigQuery, Redshift.
Advanced Airflow experience, including architecting and operating large DAG ecosystems with cross-DAG dependencies, backfills, and SLA management.
Data architecture & modeling depth (Kimball, star schema, dimensional modeling, OLAP cubes, wide fact tables, slowly-changing dimensions, semantic layer design).
Experience with ETL / ELT architecture, including incremental loads, backfills, idempotency, data quality frameworks, and lineage.
Python for data work (pandas, PySpark, scripting, light tooling development).
Deep, hands-on production experience building executive-grade dashboards in Tableau and/or Apache Superset.
Strong opinions on dashboard design, including headline vs. drilldown metrics, layout, filters, performance, and 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.
Native or near-native English (spoken and written).
Track record of leading initiatives end-to-end with minimal direction.
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.
Executive presence – ability to present analytics work to Director/VP-level stakeholders and defend recommendations.
Ownership mindset, operating as a permanent employee even in a contract role.

Nice To Haves

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.
Comfort reading experiment results and challenging methodology when needed.

Responsibilities

Partnering with Data Scientists and Product on analytics problems, driving metric definitions, reviewing analyses, and setting standards for analytics work.
Architecting and owning production SQL pipelines, data models, and data cubes; designing and operating Airflow DAGs; setting standards for data quality, reliability, and reconciliation.
Owning executive-visibility dashboards in Tableau / Superset; defining and governing metrics; enabling self-serve analytics.
Serving as the senior analytics IC for the Monetization Analytics pod, tackling ambiguous data problems.
Improving the quality of questions before answering them by reframing vague asks.
Leading end-to-end analytics initiatives with minimal supervision and clear stakeholder communication.
Setting metric definitions and standards for advertiser revenue, monetization performance, funnel/cohort metrics, and experiment readouts.
Independently driving root-cause analysis on data discrepancies across dashboards, warehouses, or pipelines.
Reviewing, coaching, and raising the bar on the work of other analysts and analytics engineers.
Architecting and owning production-grade SQL data pipelines (Presto / Trino / Hive / Spark SQL), making tradeoffs on refresh strategies, pre-aggregation, and cost/performance.
Designing and owning data cubes, aggregate tables, and semantic layers.
Authoring, owning, and operating Airflow DAGs for critical revenue and monetization pipelines, including SLAs, on-call, backfills, and incident response.
Setting and enforcing standards for data quality, reconciliation, and observability.
Optimizing existing pipelines for cost and latency.
Contributing to cross-team technical decisions via design docs and reviews.
Owning the design and quality of executive and cross-functional dashboards in Tableau and/or Superset.
Driving metric governance, including definitions, owners, source-of-truth queries, validation, and deprecation.
Enabling self-serve analytics for Data Scientists, Analysts, and PMs.