Data Engineer (Databricks)

Fluent is building the next generation advertising network, Partner Monetize & Advertiser Acquisition. Our vision is to build an ML/AI first network of advertisers and publishers to achieve a common objective, elevating relevancy in E-commerce for everyday shoppers.

As a Data Engineer on Fluent's Data Engineering team, you will bring your Databricks development expertise to build the data products that power Fluent's Audience Solutions business: the syndicated and custom consumer segments we build, model, and deliver to leading data marketplaces. These data products translate raw survey and behavioral signals into declared and modeled audiences that our commercial teams and clients rely on every day.

You are known as a strong and efficient IC Data Engineer, with the ability to assist in designing engineering solutions, data modeling, and building architecture. You are familiar with the Databricks Lakehouse and how to work backwards from translating business requirements into engineering solutions. You are considered an expert in Python, SQL, and Spark.

You will work closely with the Data Engineering team and Data Science partners to build and operate high-impact audience data solutions. This role is fully Remote in the United States or Canada, with occasional travel to NYC.

What You'll Do:
- Majority of the role will be data engineering: tables, views, jobs/pipelines, and orchestration within the Databricks environment, following Fluent’s data model and architecture best practices. You will help elevate standards across testing, code repository, ci/cd, and naming conventions.
- Build and maintain pipelines that create and deliver syndicated and custom audience segments, spanning declared (survey-based) and modeled (ML driven) methodologies.
- Support delivery and performance reporting of finished segments on distribution marketplaces such as LiveRamp, TruAudience (TransUnion), and Lotame.
- Investigate and leverage Databricks' latest capabilities for real-time processing and operational workloads, such as Spark structured Streaming and Lakeflow Declarative Pipelines.
- Contribute to and maintain a high-quality codebase with comprehensive data observability, metadata standards, and best practices, including compliant handling of consumer data in line with Fluent's data privacy standards.
- Apply AI-assisted development practices, using tools such as Claude Code, to accelerate pipeline development, testing, and documentation, while upholding Fluent's engineering and data quality standards.
- Partner with the Data Engineering team, Data Science, and commercial stakeholders to define business requests and translate them into functional requirements.
- Keep track of emerging tech and trends within the Databricks ecosystem.
- Share your knowledge by giving brown bags, tech talks, and evangelizing appropriate tech and engineering best practices.
- Empower internal teams by providing communication on architecture, execution plans, releases, and training.