Senior Software Engineer

Entravision

11d•$210,000 - $250,000•Onsite

About The Position

We are building the next generation of identity resolution to support performance CTV, mobile user acquisition, app-to-web, CTV-to-web. We are hiring a Senior Software Engineer within the Data Engineering team to define and build the identity graph foundations that connect raw identifiers, devices, people, and households into reliable, production-grade identity signals. This role sits close to Data Engineering, Data Quality, Machine Learning, and Product. The initial focus is to understand Smadex’s current pipelines and business needs, identify gaps, and build an incremental roadmap that delivers measurable value quickly while moving toward a scalable long-term identity architecture. Identity is becoming a core capability for Smadex’s next stage of growth: CTV performance and attribution: Improve household-level understanding so Smadex can better connect CTV exposure to downstream mobile and web outcomes. Mobile UA resilience: Strengthen targeting and modeling as deterministic device ID coverage continues to decline, especially on iOS. Cross-channel targeting: Enable a future where CTV, mobile, app-to-web, and CTV-to-web campaigns can be optimized through a unified identity layer. Customer data onboarding: Support use cases where customers provide hashed emails, RAMP IDs, UID2s, first-party data, or other identifiers for lookalike modeling, retargeting, and deeper integrations. Incrementality narrative: Help Smadex explain and measure the value of CTV and cross-channel campaigns beyond siloed attribution metrics.

Requirements

7+ years of experience in software engineering, data engineering, ML infrastructure, identity systems, ad tech, or related fields.
Experience designing and operating large-scale data pipelines using technologies such as Spark, Airflow, distributed processing frameworks, or equivalent systems.
Strong understanding of data modeling, data quality, pipeline reliability, observability, and production operations.
Experience working with cross-functional teams across Engineering, ML, Product, and business stakeholders.
Demonstrated ability to lead technical initiatives from ambiguous problem statements to delivered systems.
Ability to communicate complex technical concepts clearly to both technical and non-technical stakeholders.

Nice To Haves

Experience with identity graphs, household graphs, device graphs, customer data onboarding, or cross-device resolution.
Experience in DSPs, ad tech, mobile measurement, CTV, performance marketing, or real-time bidding systems.
Familiarity with identifiers such as IDFA, GAID, cookies, IP-derived signals, RAMP IDs, UID2, hashed emails, or first-party customer data.
Experience evaluating third-party data providers or identity providers.
Experience creating ML features from identity, behavioral, event-level, or user-level data.
Experience hiring, mentoring, or building a small team from scratch.
Experience improving developer productivity through CI/CD, data tooling, Airflow, Spark, or workflow improvements.

Responsibilities

Define Smadex’s identity resolution roadmap across short-, medium-, and long-term horizons.
Translate high-level business needs into concrete data engineering and ML requirements.
Build an incremental delivery plan that avoids a long “big bang” approach and validates value through clear checkpoints.
Partner with Product to prioritize identity use cases across CTV, mobile UA, app-to-web, CTV-to-web.
Help determine where third-party identifiers such as RAMP IDs, UID2, hashed emails, and other provider signals should fit into Smadex’s identity strategy.
Design and build identity graph components that map from raw identifiers to device, person, and household-level entities.
Develop approaches for resolving noisy or inconsistent identifiers from SSPs, suppliers, devices, cookies, IP signals, and other sources.
Build simple proof-of-concept pipelines quickly, then iterate toward more sophisticated graph logic.
Use behavioral patterns, activity signals, IP neighborhoods, and clustering methods to determine which identifiers likely belong together.
Ensure the architecture can support both ML model features and functional product use cases.
Work inside the Data Engineering organization to align identity work with feature engineering and data quality initiatives.
Collaborate with data engineers to define the upstream features and raw data needed for identity resolution.
Anticipate data needs 3–4 months ahead to reduce repeated back-and-forth with upstream teams.
Partner with the Data Quality function to validate ingestion quality, identifier consistency, provider reliability, and graph stability.
Build pipelines that are production-ready, observable, scalable, and maintainable.
Collaborate with ML teams to turn identity outputs into useful model features.
Evaluate whether identity-derived features improve targeting, attribution, incrementality, and model performance.
Support product and customer-facing initiatives that require identity, even when the immediate ML lift is uncertain.
Help define success metrics across both performance impact and product enablement.
Provide technical guidance for experimentation, offline evaluation, and online testing of identity-based approaches.