Senior Data Engineer (Data Architecture)

Wpromote

9h•$105,000 - $135,000•Remote

About The Position

As a Senior Data Engineer (Data Architecture), you will help design, build, and evolve the data infrastructure powering Polaris, Wpromoteâ€™s proprietary technology platform. You will work across Product, Data Engineering, and client teams to build reliable data solutions while helping us move from primarily client-specific implementations toward standardized, reusable, and scalable data capabilities. This role will spend approximately 50â€“75% of its time supporting shared data products, platform capabilities, and data architecture, with the remaining time focused on individual client pipelines and solutions. As our platform grows, we need to identify where common client requirements can become shared capabilities - particularly across strategic verticals such as Retail Media, B2B, and other emerging areas of the business. You will initially own components and technical designs within our broader architecture, with the opportunity to take increasing ownership of shared architectural patterns and data products. A major focus of this role will be strengthening our engineering foundations around data quality, standardization, testing, observability, and maintainability. At Wpromote, we believe that great work is only possible with great people. Our goal is to build a better, more inclusive work environment and support our people at every stage of their careers by prioritizing a strong work-life balance through our policies and benefits listed below. As a Best Place to Work according to both Ad Age and Glassdoor and Adweekâ€™s Fastest Growing Digital Agency, we are moving fast to expand our teams and bring new experts into the fold to keep pushing the boundaries of whatâ€™s possible in marketing.

Requirements

Bachelorâ€™s degree in Computer Science, Information Technology, Engineering, or a related field, or equivalent practical experience.
5+ years of experience in data engineering, analytics engineering, data architecture, or a related field.
Demonstrated experience independently designing and delivering production data pipelines and data models.
Advanced proficiency in SQL and BigQuery, with strong knowledge of relational and analytical database concepts.
Intermediate to advanced programming skills in Python.
Strong experience with dbt, including data modeling, testing, documentation, and reusable development patterns.
Strong understanding of data warehousing, dimensional modeling, data transformation, and data quality principles.
Experience improving or refactoring data systems for greater reusability, reliability, and scalability.
Familiarity with orchestration technologies such as Airflow, Dagster, AWS Glue, or Azure Data Factory, along with modern software engineering practices including Git, code review, CI/CD, and testing.
Strong problem-solving and communication skills, including the ability to navigate ambiguous requirements, evaluate technical tradeoffs, and communicate decisions to technical and non-technical stakeholders.
Strong business and marketing acumen, including familiarity with common marketing goals, KPIs, channels, and tactics.
Hands-on experience using AI-assisted software engineering tools such as Claude Code, OpenAI Codex, OpenCode, Cursor, GitHub Copilot, or similar tools, with the engineering judgment to review, test, and validate AI-generated solutions.
Experience within an advertising agency, consulting organization, or other multi-client environment, including mentoring or providing technical guidance to other engineers.

Nice To Haves

Experience building reusable, configuration-driven, or multi-client data pipelines and consolidating bespoke implementations into shared solutions.
Experience with data observability, lineage, metadata management, or data quality platforms.
Experience creating and consuming APIs to ingest and process large datasets.
Experience with Retail Media, B2B, ecommerce, CRM, or other specialized marketing data.
Experience working with digital media and analytics platforms such as Google Ads, Meta, The Trade Desk, Amazon Ads, Google Analytics, or Adobe Analytics.
Experience developing agentic software engineering workflows or helping establish AI-assisted development practices and guardrails within an engineering team.

Responsibilities

Designing, building, deploying, and maintaining scalable data pipelines and shared data products using BigQuery, dbt, Python, and orchestration frameworks.
Partnering with Product, Engineering, and client teams to translate business requirements into reliable and scalable technical solutions.
Identifying recurring patterns across client implementations and turning them into reusable models, frameworks, and shared capabilities, including solutions for verticals such as Retail Media and B2B.
Developing reusable dbt models, macros, Python utilities, ingestion patterns, and configuration-driven solutions that balance standardization with client-specific flexibility.
Strengthening data quality and reliability through automated testing, validation, reconciliation, monitoring, and observability.
Improving existing pipelines and architecture for greater scalability, maintainability, performance, and cost efficiency.
Integrating and normalizing data from APIs and third-party platforms across the MarTech and AdTech ecosystem.
Managing production data pipelines across Wpromoteâ€™s client portfolio, troubleshooting complex issues, and identifying systemic improvements that prevent recurrence.
Contributing to technical design, architecture discussions, code reviews, and engineering standards while providing technical guidance to other Data Engineers.
Using AI-assisted development tools to accelerate development, codebase exploration, debugging, testing, documentation, and code review while maintaining accountability for production code and sharing effective practices with the broader team.