Senior Data Engineer (Data Architecture)

About the role

The Senior Data Engineer (Data Architecture) will be responsible for designing, building, and evolving the data infrastructure for a proprietary technology platform. This role involves working closely with product development, engineering, and client-facing teams to create reliable and scalable data capabilities.

Responsibilities

• Design, build, deploy, and maintain scalable data pipelines and shared data products using BigQuery, dbt, Python, and orchestration technologies.
• Translate business and client requirements into reliable, scalable, and maintainable technical solutions.
• Identify recurring patterns across client implementations and transform them into reusable models, frameworks, ingestion patterns, utilities, and shared capabilities.
• Develop configuration-driven solutions that balance standardization and scalability with the flexibility required by individual clients.
• Strengthen data reliability through automated testing, validation, reconciliation, monitoring, documentation, and observability practices.
• Improve existing data pipelines and architecture to increase scalability, maintainability, performance, and cost efficiency.
• Integrate, normalize, and process data from APIs and third-party platforms across the MarTech and AdTech ecosystem.
• Manage production data pipelines across a diverse client portfolio, troubleshoot complex issues, and identify systemic improvements that reduce recurring problems.
• Contribute to technical designs, architecture discussions, code reviews, engineering standards, and best practices.
• Provide technical guidance and mentorship to other Data Engineers while helping improve overall engineering effectiveness.
• Help evolve client-specific implementations into shared data products, frameworks, and vertical-focused capabilities, including Retail Media and B2B solutions.

Requirements

• Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related discipline, or equivalent practical experience.
• 5+ years of experience in data engineering, analytics engineering, data architecture, or a related field.
• Advanced SQL and BigQuery expertise, with strong knowledge of relational databases, analytical databases, data warehousing, and dimensional modeling.
• Intermediate to advanced Python programming skills.
• Strong hands-on experience with dbt, including data modeling, testing, documentation, and reusable development patterns.
• Prominent experience improving or refactoring data systems for greater reliability, scalability, reusability, and maintainability.
• Familiarity with orchestration technologies such as Airflow, Dagster, AWS Glue, or Azure Data Factory.
• Experience with modern software engineering practices, including Git, code reviews, CI/CD, automated testing, and production support.
• Strong understanding of data quality principles and the ability to implement effective validation and monitoring approaches.
• Strong problem-solving and communication skills, with the ability to navigate ambiguity, evaluate technical tradeoffs, and communicate decisions to both technical and non-technical stakeholders.
• Solid business and marketing understanding, including familiarity with marketing objectives, KPIs, channels, and tactics.
• Hands-on experience with AI-assisted software engineering tools such as Claude Code, OpenAI Codex, OpenCode, Cursor, GitHub Copilot, or similar technologies, combined with the judgment to review, test, and validate AI-generated code.
• Experience working within an advertising agency, consulting organization, or other multi-client environment is preferred, particularly experience mentoring or technically guiding other engineers.
• Experience with reusable, configuration-driven, or multi-client data pipelines is a plus.
• Familiarity with data observability, lineage, metadata management, or data quality platforms is advantageous.
• Experience creating or consuming APIs and processing large datasets is beneficial.
• Experience with Retail Media, B2B, ecommerce, CRM, or specialized marketing data is a plus.
• Familiarity with platforms such as Google Ads, Meta, The Trade Desk, Amazon Ads, Google Analytics, or Adobe Analytics is advantageous.
• Experience establishing AI-assisted engineering workflows, agentic development practices, or engineering guardrails is a plus.

Benefits

• Annual salary: $105,000–$135,000, depending on experience, skills, education, certifications, location, market conditions, and other job-related factors.
• Remote-first culture, with the role available remotely in most U.S. states, subject to location exclusions.
• Unlimited PTO and an extended winter holiday break.
• Flexible work schedules designed to support work-life balance.
• 100% paid parental leave.
• 401(k) matching.
• Medical, dental, vision, life, and pet insurance.
• Short-term disability insurance and additional voluntary insurance options.
• Sponsored life insurance.
• Annual ClassPass credits and additional employee perks.
• Access to office hubs in Los Angeles, Chicago, and New York for collaboration, events, learning, and development opportunities.
• Opportunity to work on impactful technology and data initiatives in a fast-growing environment.
• Career development opportunities and exposure to modern data engineering, architecture, and AI-assisted development practices.

Pay

Annual salary: $105,000–$135,000, depending on experience, skills, education, certifications, location, market conditions, and other job-related factors.

Schedule

Remote-first culture, with the role available remotely in most U.S. states, subject to location exclusions.