Data Engineer, AI & Analytics

Who We Are:

Power Digital is an AI-Native Growth Firm built at the intersection of world-class talent and proprietary technology. We help leading and emerging brands grow by combining deep vertical expertise, strategic thinking, and enterprise intelligence to drive measurable business outcomes.

For more than a decade, we have built our business around one belief: the best technology makes great people better. Our teams of strategists, creatives, analysts, and technologists use Omega, our AI operating system, to surface opportunities faster, investigate what is driving performance, and execute approved work with greater speed and precision. Behind Omega is Iris, our proprietary intelligence layer, built on years of real performance data, strategic frameworks, testing, and learnings from across our client portfolio. AI expands the capacity of our people to think, create, solve harder problems, and make better decisions. Human judgment, creativity, and accountability remain central to how we work.

As a people-first firm, we value diversity in backgrounds and experiences. We strongly believe our people and culture are key to our success. We partner closely with our clients as an extension of their teams, working across strategy, execution, and measurement to identify what is truly blocking growth and determine what to do next. Our culture is built on people who are curious, ambitious, accountable, and committed to delivering meaningful results for our clients and for one another.

At Power Digital, we believe the future belongs to teams that combine the best talent with the best technology. We are building that future today.

***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.