Senior Data Engineer (Data Architecture)

You Will Be 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 Wpromote’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.

You Must Have Bachelor’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 Have 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.

What Success Looks Like Success in this role will center around three areas: Delivery: You will become a trusted owner of complex data pipelines and shared components, independently delivering reliable, maintainable solutions while effectively supporting both product and client needs. Standardization: You will strengthen our engineering foundations through improved data quality, testing, observability, documentation, and reusable development patterns, helping establish consistent standards for how data solutions are built across the organization. Architecture: You will identify patterns across client implementations and help evolve them into shared data products, frameworks, and vertical-specific capabilities, taking increasing ownership of architectural decisions as you grow in the role.