Data Engineer Marketing Specialist

About the Job

Delivering high-impact data solutions is our top priority. We need a skilled data engineer to join us as a marketing platform specialist.

The ideal candidate will be responsible for extracting valuable insights from various marketing platforms, focusing on attribution data, auction signals, and algorithmic behavior.

The key objectives include:

• Reverse-engineering APIs across Meta, Google, TikTok, and other prominent platforms to uncover hidden attribution data, auction signals, and algorithmic behavior
• Unlocking advanced data streams by exploiting beta features and undocumented endpoints
• Capturing ephemeral data before it disappears – attribution snapshots, pacing drift, algorithm shifts
• Building real-time monitoring datasets to detect anomalies, pacing issues, and creative decay
• Scraping competitor websites and dissecting tracking logic to reveal platform strategies

Data Analysis & Optimization Datasets

We're looking for someone who can build granular spend and performance datasets with dayparting, marginal ROAS, and cost efficiency metrics.

Develop lookalike audience models enriched with seed performance, overlap scores, and fatigue indicators.

Create auction intelligence datasets with hour/geo/placement granularity, bid behaviors, and overlap tracking.

Design optimization datasets for portfolio performance, campaign cannibalization, and creative lifecycle decay.

Extract machine learning signals from Meta Advantage+, Smart Bidding configs, and TikTok optimization logs.

Build competitive intelligence datasets with SOV trends, auction pressure, and creative benchmarks.

Advanced Data Engineering

We need a data engineer who can extract structured data from advanced features like predictive analytics, customer match, and A/B testing tools.

Build multimodal datasets (ad copy, landing pages, video) ready for machine learning workflows.

Enrich historical marketing data using automated pipelines and AI-powered data cleaning.

Unified Customer & Attribution Data

Our ideal candidate will build comprehensive GA4 datasets using precise tagging logic and event architecture.

Unify data from Firebase, Klaviyo, Tealium, and offline sources into full-funnel CDP datasets.

Engineer identity resolution pipelines using hashed emails, device IDs, and privacy-safe matching.

Map cross-device customer journeys with detailed attribution paths and timestamp precision.

Data Architecture & Analytics Enablement

We're seeking a data engineer who can design and maintain scalable, semantic-layered Snowflake datasets with historical tracking and versioning.

Use S3 and data lakes to manage large-scale, unstructured data across channels.

Implement architectures suited for exploration, BI, real-time streaming, and ML modeling – including star schema, medallion, and Data Vault patterns.

Build dashboards and tools that reveal inefficiencies, scaling triggers, and creative performance decay.

Requirements

• At least 3 years of experience as a data engineer with deep knowledge in MarTech or growth/performance marketing
• Advanced Python skills for API extraction, automation, and orchestration
• JavaScript proficiency for tracking and GTM customization
• Expert-level experience with GA4 implementation and data handling
• Hands-on experience with Snowflake, including performance tuning and scaling
• Comfortable working with S3 and data lakes for semi/unstructured data
• Strong SQL and understanding of scalable data models

Bonus Points if You Have

• Experience with dbt for transformation and modeling
• CI/CD pipelines using GitHub Actions or similar
• Knowledge of vector databases (e.g., Pinecone, Weaviate) for AI/ML readiness
• Familiarity with GPU computing for high-performance data workflows
• Data app development using R Shiny or Streamlit

What We're Looking For

• Relentlessly curious: You don't wait for someone to show you the API docs; you find the endpoints yourself
• Detail-obsessed: You notice the subtle changes, the disappearing fields, the data drift others overlook
• Self-directed: You don't need micromanagement. Just give you the mission, and you'll reverse-engineer the map
• Comfortable with ambiguity: You can navigate undocumented features, partial datasets, and platform quirks without panic
• Great communicator: You explain technical decisions clearly, with just enough detail for analysts, marketers, and fellow engineers to move forward
• Product-minded: You think in terms of impact, not just pipelines. Every dataset you build is a stepping stone to better strategy, smarter automation, or faster growth

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