• Make Large Datasets Feel Light : Implement level-of-detail downsampling, viewport culling, sub-pixel zoom/pan, and time-aligned overlays at interactive frame rates on tablets • Render the PQ Vocabulary : Build oscillographic waveforms with cycle markers, RMS-vs-time strip charts, harmonic spectrum bars to the 50th order, phasor diagrams, harmonic heatmaps, CBEMA/ITIC tolerance curves, scatter/PDF plots, and event timelines • Design Analysis-Grade Interactions : Smooth pinch/scroll on touch, trackpad, and mouse-wheel; crosshair with multi-series readout; brush-to-zoom; synchronized cursors across stacked charts; drill-down on event markers • Get Visual Details Right : Color palettes that work on screen and print, accessible contrast, type and tick spacing respecting expert reading habits • Profile Relentlessly : Performance benchmarking and profiling to improve user experience and optimize metrics footprint • Ship Usable v1, Then Iterate : First cut of a new chart in a week, refined with team and real users in the open • Operate the Chart Engine : Share on-call rotation for rendering and performance regressions, turning each into a permanent benchmark

• Build and operate ingestion, ELT/ETL, and orchestration pipelines that move data from our MongoDB Atlas operational store and other sources into our analytical and AI-serving layers • Implement layered (medallion-style) transformations with idempotent, backfillable, incrementally loaded jobs • Apply deduplication, normalization, and validation so downstream data is high-quality and trustworthy • Modernize legacy / homegrown data flows via incremental, strangler-fig migrations that keep production stable • Build embeddings and vector pipelines, and the feature/retrieval-ready datasets that RAG, semantic search, and agentic workloads depend on • Make production data AI-ready in practice: well-structured, lineage-tracked, and retrieval-friendly, in partnership with ML and application engineering • Implement real-time and change-data-capture flows from MongoDB (Change Streams / CDC) where workloads require fresh data • Implement the canonical data model, schemas, and data contracts defined by the Data Architect — enforced in-repo so other teams build against stable definitions • Exercise sound persistence judgment in execution: land data in the right store (document / NoSQL, vector, analytical) per the architectural direction • Contribute to build-vs-buy decisions by prototyping with proven, industry-standard tooling over custom development • Establish testing, data-quality, and lineage checks for the pipelines you own, with clear alerting and runbooks • Instrument pipeline observability (freshness, volume, schema-drift, cost) so failures are caught before consumers feel them • Use AI-assisted development tools (Claude Code, Copilot, Cursor) as a force multiplier for transformation logic, query tuning, and migration scripting • Partner with database engineering on extracting from and protecting the production store • Partner with the Data Architect on implementing target-state patterns and surfacing what's hard to build • Partner with ML, AI, and application engineers on the data they consume — shaping and governing it so it's safe and ready to build on

• Join a motivated, career-oriented team as a Data Engineer. • Design, build, and maintain ETL pipelines across diverse data sources. • Implement data management standards and ensure data quality practices. • Collaborate with teams to optimize data loading and management processes.

Title: Data Engineer Locations: Los Angeles; New York; Remote; San Francisco Hybrid Job Description: Who We Are EDO is the TV outcomes company. Our leading measurement platform connects convergent TV airings to the ad-driven consumer behaviors most predictive of future sales. EDO empowers the advertising industry to maximize media impact, optimize creative performance, and know the fair value of every impression - across linear and streaming for an increasingly programmatic world. By combining immediate engagement signals with world-class decision science and vertical AI, EDO equips industry leaders with syndicated, investment-grade data that aligns media to business results - with detailed competitive, category, and historical insights. Leading brands, agencies, networks, streamers, and studios trust EDO's TV intelligence to know what works. EDO is headquartered in New York City and Los Angeles with an office space in San Francisco. We recognize the benefits of hybrid working, and want to create the best balance to ensure we can continue working together effectively. For those who reside full-time in NYC, SF, or LA: we have a hybrid work policy of three days in the office and two remote work days. The Role As a Data Engineer at EDO, you will join a team of talented engineers working closely with Data Scientists to build and scale our next-generation Ad EnGage data pipeline. This pipeline measures and optimizes the effectiveness of advertising by combining and analyzing a wide range of datasets, including advertising occurrence data, outcome data, identity resolution data, and more. Our clients rely on these insights to make critical decisions about their advertising strategy. You will work with large-scale datasets (hundreds of TBs to petabyte-scale systems) using a modern data stack centered on AWS, Airflow, dbt, and Snowflake. You'll contribute to building reliable, high-quality data pipelines and improving the performance, scalability, and observability of our data platform. What We Are Looking For - 3-6 years of experience as a Data Engineer. - Hands-on experience with cloud data platforms (Snowflake or Redshift preferred). - Strong understanding of data warehousing concepts and cloud infrastructure (AWS preferred). - Experience building and maintaining ETL/ELT pipelines using tools such as Airflow and dbt. - Strong SQL skills, including query optimization, data modeling, and working with large datasets. - Proficiency in at least one scripting language, preferably Python. - Hands-on experience with large-scale, complex data pipelines operating at billions to trillions of rows of data. - Ability to work independently on well-scoped projects, collaborate with cross-functional partners, and deliver production-ready solutions. Compensation and Benefits EDO offers a competitive compensation package. Components of compensation include: - Mid-stage equity and competitive salary - Flexible Time Off - Medical, dental and vision coverage: EDO provides full coverage for individual medical plans and partial coverage for dependent or family plans. - 401(k) plan, FSA, HSA - Commuter Benefits - When in an office, employee meals, snacks, and more In compliance with New York and California Pay Transparency Law, the salary range for this position is $120,000 to $160,000 plus equity. We note the salary information as a general guideline only, as actual compensation may vary from posting. We will consider various factors to determine the offer for this role, including the scope and responsibilities of the position, relevant work experience, key skills, training, and business considerations.