***Proficiency in spoken and written English at an advanced level is required for this role.

A day in the life:

You'll sit on the Data Team, which owns the core data foundation for Power Digital: the pipelines, modeling, and data marts that power our agency teams, clients, and AI initiatives. You'll work end to end, from raw ingestion through the semantic layer, using AI-agentic workflows as a normal part of how you build.

The data itself is the interesting part. Marketing data is fragmented by default. Every ad platform has its own API, its own schema, and its own definition of a conversion. Platforms restate attributed conversions days after the fact, each in a different way. Entity hierarchies don't match (campaign/ad set/ad on Meta, campaign/ad group/ad on Google). Naming conventions, currencies, and timezones vary by client.

We do this across a large client portfolio, each client with a different stack, in a warehouse with per-client tenancy. You'll work closely with Client Service, BI, Tagging & Tracking, Data Ops, and the nova product/engineering teams.

Key Responsibilities:

• Design, build, and maintain the core data foundation (ingestion, modeling, and data marts), owning the workflow from raw platform data through the serving layers that support agency, client, internal, and AI consumers.
• Build ingestion that handles what ad platforms actually do: API changes, deprecated fields, aggressive rate limits, and retroactive restatement of conversion data, all without corrupting downstream models.
• Model across sources so the numbers reconcile: spend, impressions, conversions, and revenue across Meta, Google, TikTok, Amazon, LinkedIn, and Microsoft, plus customer-level joins across Shopify, Klaviyo, GA4, and client CRMs.
• Contribute to client-bespoke modeling on top of the core layer: custom logic, overrides, and client-specific marts built in response to individual client requests, with patterns that extend the shared foundation rather than fork it. This is steady, recurring work, not an occasional exception.
• Build the semantic layers and metric definitions that let AI-generated SQL return consistent, correct answers.
• Use AI-agentic workflows, including AI coding tools, to accelerate development and build intelligent data infrastructure. Document what works so it becomes a standard team pattern.
• Collaborate cross-functionally with nova (product and engineering), AI/innovation, and client teams to translate requirements into data solutions and support rapid iteration on new products and features.
• Monitor and resolve data quality issues, and optimize pipelines for cost and performance across a multi-client warehouse.

Role Requirements:

• 3+ years in data or analytics engineering, including 1+ years owning a dbt project of meaningful size in production, not just contributing models to one.
• Advanced proficiency in Python and SQL, with a focus on production-grade code for data pipelines and modeling.
• Deep expertise in dbt, not just writing models. Incremental strategies and full-refresh tradeoffs, Jinja and macros, packages, generic and singular tests, snapshots, source freshness, exposures, and how to keep a large project's DAG and materializations under control.
• Strong command of Snowflake and the surrounding cloud data stack to operate autonomously as a foundational data owner.
• Experience modeling in a multi-tenant environment, with the judgment to know when a client request belongs in a client layer and when it belongs in the core.
• Working knowledge of marketing and advertising datasets. You've handled UTMs, attribution windows, and the gap between platform-reported and warehouse-reported conversions.
• Proven experience designing and managing end-to-end data lifecycles from ingestion to serving, with reliability that holds for both BI and AI applications.
• Familiarity with cloud-native infrastructure (GCP) and infrastructure-as-code principles.
• Real adoption of AI-agentic development workflows (Cursor, Claude Code, GitHub Copilot) for coding, debugging, and system architecture.
• Demonstrated ability to architect AI-ready data models (feature stores, clean semantic layers) that support downstream initiatives.
• Experience with Git and CI/CD best practices, including automated testing you trust.
• Comfortable shipping iteratively and refining data products based on live feedback.

 

Helpful but not required

• Agency, consultancy, or services experience. Working across many clients with different stacks transfers directly.
• Measurement work: incrementality, media mix modeling, attribution.
• Server-side tagging, or retail and marketplace data.

You might be a fit if…

• You're a data engineer at a brand or retailer and want to work closer to the decisions your data drives.
• You've built marketing pipelines at an agency or martech company and know how they break.
• You're an analytics engineer who wants to own the full system rather than just the modeling layer.
• You've rebuilt your own workflow around AI coding agents and want that to be the job.

Key Performance Indicators (KPIs)

• AI-accelerated development. Reduce median development time from approved requirements to production deployment for new data pipeline and modeling requests by 20% within the first 6 months, using the team's established baseline for comparable requests. Document and productionize at least 1 reusable AI-agentic development pattern within the first 90 days and at least 2 within the first 12 months, with each pattern adopted in at least 2 production workflows or projects.
• Data quality and reliability. Maintain ≥99% accuracy and completeness across fields designated as critical for client-facing and AI-facing data products, measured through automated data quality tests and reconciliations. Maintain a ≥95% successful scheduled pipeline execution rate for owned production pipelines, excluding documented upstream vendor/platform outages.
• Client request throughput. Deliver ≥90% of assigned client-bespoke modeling requests within the agreed-upon turnaround time, measured quarterly. Where a bespoke request introduces logic applicable across clients, evaluate and document whether it belongs in the shared core or client-specific layer for 100% of material modeling changes.
• Cross-functional enablement. Launch or materially migrate at least 2 major production data assets within the first 12 months that support agency, client, product, or AI consumers. Within 90 days of each launch, demonstrate adoption through at least 2 active downstream consumers, applications, or teams per asset and achieve either a 20% reduction in related recurring data-support tickets or another pre-defined adoption/efficiency target agreed upon before launch.

Most Important Things (MITs)

• Build and ship end-to-end data systems that enable AI features.
• Deliver production-ready datasets and pipelines that unblock AI, product, and client teams.
• Reduce fragmentation by building unified, AI-ready data foundations