Data Engineer

Job Description:

We are seeking a highly skilled Data Engineer to design, build, and maintain our Audience Household Graph, a core data asset that connects people, devices, and households to enable cross-platform audience activation, targeting, and measurement across the digital advertising ecosystem.

This role will work closely with data scientists, product managers, and ad tech partners (DSPs, SSPs, and clean rooms) to unify fragmented identifiers and power scalable audience solutions for programmatic, CTV, and omnichannel campaigns.

Key Responsibilities:

Data Architecture & Pipeline Development:

Build and maintain scalable data pipelines that ingest, process, and unify multi-source identity data (device IDs, IPs, emails, cookies, CRM, CTV IDs) into a graph-based model.

Graph Modeling:

Design and optimize graph data structures representing relationships between individuals, devices, and households to support audience matching, lookalike modeling, and reach deduplication.

Ecosystem Integration:

Integrate data from DSPs, SSPs, DMPs, CDPs, and clean rooms to enable audience onboarding, enrichment, and campaign measurement.

Data Quality & Governance:

Establish robust data validation, deduplication, and probabilistic/deterministic matching pipelines to ensure accuracy and compliance with privacy regulations (GDPR, CCPA).

Scalability & Performance:

Ensure the household graph supports real-time or near-real-time audience activation across millions of households and billions of identifiers.

Collaboration:

Partner with Data Science, Product, and Ad Operations teams to operationalize audience data into DSP workflows, measurement systems, and optimization platforms.

Monitoring & Optimization:

Build dashboards and monitoring tools to track data quality, match rates, and audience graph performance across DSP integrations and media channels.

Qualifications:

Required:

Bachelor's or master's degree in computer science, Data Engineering, or related field.

4+ years of experience in data engineering, ideally within ad tech, martech, or audience data environments.

Strong proficiency in Python, SQL, and distributed data frameworks (Spark, Databricks, Snowflake, or Big Query).

Experience with graph databases (Neo4j, Amazon Neptune, or Tiger Graph).

Solid understanding of digital advertising data flows — from audience onboarding and targeting to DSP activation and measurement.

Experience integrating and normalizing large-scale identity and event-level data (e.g., ad impressions, device graphs, CRM lists, pixel data).

Familiarity with probabilistic and deterministic identity resolution methods.

Preferred:

Experience with cloud platforms (AWS, GCP, or Azure) and ETL orchestration tools (Airflow, dbt).

Understanding of programmatic advertising, including DSP/SSP data integrations, bidstream data, and audience segments.

Knowledge of data clean rooms (e.g., Snowflake, Google PAIR, Amazon Clean Rooms) and privacy-safe data collaboration frameworks.

Familiarity with measurement and attribution systems (e.g., Nielsen, VideoAmp, LiveRamp, The Trade Desk).

Strong understanding of identity graph and household graph use cases in audience targeting, frequency management, and cross-platform reach.

Key Success Metrics:

Accuracy and coverage of household and identity linkages.

Data pipeline reliability, uptime, and latency.

Quality and scalability of integrations with DSPs and data partners.

Compliance and adherence to privacy standards.

Business impact measured through improved audience reach, match rates, and measurement precision.

Must Have:

Must have experience with Data scientists, Data analytics and engineering.

• Must have experience in Media & Entertainment, OTT industry.