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Senior Data Engineer, Ads

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Remote from
USA
Salary
USD 220,500–245k / yr
Employment
Full Time
Experience
Senior
Published
Apply before
11 Nov 2026
Listing views
38
Application actions
3
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AI Summary

The role, at a glance.

This is a senior-level data engineering role focused on advertising data products, machine-learning inputs, and measurement infrastructure at Discord. The engineer will own canonical ads data models, batch and near-real-time pipelines, conversion attribution integrations, and data-quality systems operating at very large scale. The position requires deep ad-tech expertise across delivery, targeting, ranking, attribution, and mobile measurement partners, alongside expert SQL and Python skills. It is highly cross-functional, requiring close partnership with Data Science, ML Engineering, Product, GTM, and Data Platform teams. The role also carries technical leadership expectations, including architecture decisions, mentoring, and driving alignment in a greenfield environment.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThe role combines senior data engineering ownership with specialized advertising-domain knowledge, ML feature data, attribution accuracy, and low-latency infrastructure at billion-row scale. Success requires making durable architectural decisions amid incomplete requirements while influencing several technical and business functions.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job medianHighly competitive
$232,750
US market range$190k–$260k
AI insightThe disclosed US annual base-salary range is $220,500 to $245,000 USD, with a midpoint of $232,750. Equity and benefits are explicitly excluded from this base-pay range. A competitive US market base-salary range for a senior ads-focused data engineer with ML, attribution, and large-scale pipeline expertise is approximately $190,000 to $260,000 annually; Discord's disclosed range sits in the upper portion of that market range.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you design a canonical ads data model supporting delivery, measurement, targeting, attribution, and machine-learning use cases?

I would begin by defining stable business entities and event-grain contracts for impressions, clicks, conversions, campaigns, audiences, and identities. I would build conformed fact and dimension tables with documented ownership, lineage, freshness expectations, and privacy controls, then publish purpose-built aggregation and feature layers rather than allowing each consumer to redefine metrics. I would validate the model with representative reporting, attribution, and ML-training use cases before formalizing versioning and migration practices.

How would you ensure conversion-attribution parity when integrating MMP data from providers such as Adjust or AppsFlyer?

I would define a shared conversion taxonomy, identity-resolution rules, attribution windows, deduplication logic, and timezone standards before implementing ingestion. I would reconcile source records against internal events using automated aggregate and sampled event-level checks, investigate discrepancies by platform and campaign, and expose parity metrics through monitoring dashboards. Any material variance would trigger an alert, a documented root-cause process, and backfill procedures where appropriate.

Describe how you would build observability for ML input data used in ads ranking or targeting.

I would monitor freshness, volume, schema changes, null rates, cardinality, distribution drift, feature coverage, label delay, and join-loss rates at each critical stage. I would set data contracts and service-level objectives for high-impact datasets, establish automated anomaly detection with severity-based alerting, and provide lineage that lets engineers trace a degraded feature back to its upstream source. For model-critical issues, I would include safe fallback behavior and clear incident ownership.

How would you approach reducing latency for a batch-oriented ads data platform without compromising reliability?

I would first measure end-to-end latency and identify the highest-value use cases, such as model training inputs or delivery reporting, rather than streaming every workload. I would separate immutable event ingestion from serving transformations, introduce incremental processing or streaming where justified, and validate correctness through dual-run comparisons with existing batch outputs. I would roll out changes gradually with latency, cost, completeness, and accuracy guardrails.

Tell us about a time you made an important architecture decision with incomplete information.

I would explain the decision context, the assumptions I documented, and the options I evaluated against scalability, correctness, delivery speed, and operational cost. I would describe choosing an extensible minimum solution, aligning stakeholders on explicit tradeoffs, and adding measurable checkpoints so the design could evolve as requirements became clearer. A strong outcome would include both near-term delivery and a clear migration path that avoided locking the organization into brittle infrastructure.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Opportunity details

About this role.

Discord has a highly engaged community of millions of daily active users who use the platform for many different reasons, but there’s one thing that nearly everyone does: play video games. Discord plays a uniquely important role in the future of gaming, and we are focused on making it easier and more fun for people to hang out before, during, and after playing games.

Discord seeks a seasoned Data Engineer, focusing on our advertising product data. In this role, you will drive technical vision and strategy for ads data engineering in support of our ML initiatives while building and maintaining sophisticated data pipelines, datasets, and analytical tools. You will lead cross-functional initiatives to transform our advertising products through data-driven insights and mentor fellow engineers to deliver exceptional results.

This role works heavily with Data Science, Machine Learning, and product. If leading technical innovation, architecting scalable solutions, and empowering teams through data excites you, we encourage you to make a move!

What You’ll Be Doing

  • Design and own core ads data models: fact/dim tables, canonical datasets, and aggregation layers that power delivery, measurement, targeting, attribution, and ML use cases.
  • Build and maintain the ML data that enables ads ranking, delivery, and targeting – including feature development, label generation workflows, intra-day training, and ML input observability to catch data quality issues before they degrade model performance.
  • Build conversion measurement pipelines and integrate third-party attribution data – including Conversion Attribution and Mobile Measurement Partner (MMP) integrations (Adjust, AppsFlyer, Singular) – ensuring attribution accuracy and data parity across measurement surfaces.
  • Build batch and near real-time pipeline infrastructure across the ads ecosystem – pushing toward lower-latency data for ML and reporting use cases on our BigQuery + dbt + Dagster stack. Partnering with Data Platform on launch and success of new data processing engines to support low latency requirements..
  • Develop data quality frameworks, monitoring systems, automated anomaly detection, and SLA infrastructure for critical ads pipelines at massive scale.
  • Proactively identify foundational data infrastructure gaps – including those with broad implications across ML, measurement, and reporting – and design scalable, canonical solutions that multiple teams can depend on.
  • Build systems from scratch in a rapidly evolving, greenfield advertising data environment – making sound architectural decisions with incomplete information and balancing short-term delivery with long-term infrastructure investment.
  • Drive alignment across Data Science, ML Engineering, Ads Product, and GTM teams through clear narratives that connect data infrastructure decisions to business outcomes and revenue impact.
  • Mentor engineers through technical challenges, code and design reviews, and ownership of complex projects – contributing to the culture and engineering standards of the Data Engineering team team.

What you should have

  • 5+ years of hands-on experience writing production code and architecting data pipelines with high-volume consumer data in advertising technology domains (ad delivery, ranking, targeting, identity, conversion measurement).
  • Deep expertise in digital advertising data engineering – specifically in ads delivery, conversion measurement, attribution pipelines, or ML feature data infrastructure. Experience with Conversion Data and APIs, MMP integrations, or identity graph infrastructure is strongly valued.
  • Demonstrated experience building data models in a greenfield or 0-to-1 environment where requirements change frequently, documentation is sparse, and architectural decisions are made with incomplete information.
  • Expert-level SQL and Python. Strong ability to design performant, maintainable data models and write production-quality pipeline code.
  • Proven hands-on experience with data quality audits, monitoring systems, and automated anomaly detection for massive-scale datasets (billions+ rows) – including quality frameworks designed for ML inputs.
  • Strong technical communication skills with the ability to drive alignment, influence prioritization, and earn adoption from technical stakeholders.
  • Collaborative mindset and strong cross-functional instincts with experience building trusted working relationships with Data Science, ML Engineering, and Product teams.

Bonus Points

  • Passion for Discord or gaming communities
  • Experience with data visualization and dashboarding technologies (Looker, Tableau, or similar)
  • Experience with designing data architecture to power a variety of use cases, including reporting (internal and external), adhoc analysis, experimentation.
  • Working with Data AI tools to establish greater self service utility for your customers.
  • Experience building near real-time or streaming pipeline infrastructure (e.g., Kafka, Spark Streaming, or equivalent) in addition to batch processing is preferred.

The US base salary range for this full-time position is $220,500 to $245,000 + equity + benefits. Our salary ranges are determined by role and level. Within the range, individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include equity, or benefits.

Why Discord?

Discord plays a uniquely important role in the future of gaming. We’re a multiplatform, multigenerational and multiplayer platform that helps people deepen their friendships around games and shared interests, and helps developers build and grow their businesses. We believe games give us a way to have fun with our favorite people, whether listening to music together or grinding in competitive matches for diamond rank. Join us in our mission! Your future is just a click away!

Discord is committed to inclusion and providing reasonable accommodations during the interview process. We want you to feel set up for success, so if you are in need of reasonable accommodations, please let your recruiter know.

Please see our Applicant and Candidate Privacy Policy for details regarding Discord’s collection and usage of personal information relating to the application and recruitment process by clicking HERE.

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This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

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