Data Engineer

Who We Are: We are a tech-enabled growth firm–at the intersection of marketing, consulting & data intelligence–igniting revenue and brand recognition for leading and emerging companies around the world. Our vision is to be recognized as the most valued and respected private growth marketing firm in the world–with a scalable brand, culture and services. Our mission is to power the relentless pursuit of growth and redefine what's possible through a team of growth-obsessed experts who demand innovation and results
- driven by integrity, autonomy, and grit. As a full-service growth marketing firm,

we offer
best-in-class services including: SEO, Content Marketing, Paid Media, Social Media Marketing, Programmatic + CTV, Public Relations, Influencer Marketing, Email + SMS, Conversion Rate Optimization, Retail Marketing, and Creative. Here at Power Digital, we are hyper-focused on helping brands drive revenue growth and brand recognition, ultimately driving irrefutable value for our clients. At the heart of Power Digital is our proprietary technology, nova, which analyzes businesses through first-party data, simplifying investment planning for marketing and diligence in M&A––putting marketers in a strategic seat at the table––and providing value in unparalleled ways. Managing billions in media, our dynamic team––of consultative marketers, creatives, analysts and technologists––challenge traditional ways of planning and measurement through meticulous testing and data science across each milestone of the customer journey. ***Proficiency in spoken and written English at an advanced level is required for this role. 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. The data itself is the interesting part. Marketing data is fragmented by default. Entity hierarchies don't match (campaign/ad set/ad on Meta, campaign/ad group/ad on Google). You'll work closely with Client Service, BI, Tagging & Tracking, Data Ops, and the nova product/engineering teams. 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. 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. 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. 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