Senior Data Engineer (Data Architecture)
New
W
WpromoteDigital Marketing
Remote, United StatesFull-TimeSenior
Salary105,000 - 135,000 USD per year
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLGitAirflowCI/CDBigQuerydbt
Requirements
- 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.
- Experience with modern software engineering practices including Git, code review, CI/CD, and testing.
- Strong problem-solving and communication skills to navigate ambiguous requirements and communicate decisions.
- 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, GitHub Copilot, or similar.
- Experience within an advertising agency, consulting organization, or other multi-client environment.
Responsibilities
- Design, build, deploy, and maintain scalable data pipelines and shared data products using BigQuery, dbt, Python, and orchestration frameworks.
- Partner with Product, Engineering, and client teams to translate business requirements into reliable and scalable technical solutions.
- Identify recurring patterns across client implementations and turn them into reusable models, frameworks, and shared capabilities.
- Develop reusable dbt models, macros, Python utilities, and configuration-driven solutions.
- Strengthen data quality and reliability through automated testing, validation, reconciliation, monitoring, and observability.
- Improve existing pipelines and architecture for scalability, maintainability, performance, and cost efficiency.
- Integrate and normalize data from APIs and third-party platforms across the MarTech and AdTech ecosystem.
- Manage production data pipelines, troubleshoot complex issues, and identify systemic improvements.
- Contribute to technical design, architecture discussions, code reviews, and provide technical guidance to other Data Engineers.
- Use AI-assisted development tools to accelerate development while maintaining accountability for production code.
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