Data Engineer, Go To Market (Remote)

📋 Description

• • Lead the full lifecycle of data engineering projects, from requirement gathering with stakeholders to production deployment and monitoring, specifically supporting Go-To-Market functions.
• • Design, develop, and maintain complex data transformations using Python, DBT, and Apache Airflow to ensure high data quality, performance, and scalability across Operational Data Store and Enterprise Data Lake platforms.
• • Build, scale, and maintain automated data workflows using Apache Airflow to manage sophisticated dependencies and ensure reliable pipeline execution.
• • Implement CI/CD practices with GitHub for version control and automated testing to uphold high engineering standards across data pipelines.
• • Develop automated data validation processes to guarantee data accuracy, integrity, and consistency across all ingestion and transformation pipelines.
• • Partner with Analytics, Sales, and Marketing teams to build curated, business-ready datasets that enable strategic decision-making and performance tracking.
• • Architect scalable DBT projects using advanced modeling techniques, custom macros, and Jinja-templated logic to enforce DRY principles and modular structure across enterprise data models.
• • Manage the complete DBT lifecycle including integration with CI/CD tools such as Jenkins or GitLab CI/CD and source control via GitHub.
• • Identify, diagnose, and resolve data quality issues by cleaning, preparing, and optimizing data for consumption by downstream analytics and reporting systems.
• • Integrate business data from enterprise applications into Semantic Layers to decouple complex logic from the BI layer and enhance analytical agility.
• • Collaborate with internal and external stakeholders to address data-related technical issues and support infrastructure needs for marketing and sales operations.
• • Work with teammates to review design, code, and test plans to elevate team knowledge and ensure consistent application of data engineering frameworks and methodologies.
• • Ensure data pipelines effectively process and transform marketing datasets including campaign performance data, lead funnel stages, opportunity pipelines, and revenue attribution models.
• • Maintain compliance with enterprise data governance standards while delivering scalable, reusable, and well-documented data solutions.
• • Contribute to the continuous improvement of data architecture by evaluating new tools, technologies, and best practices in modern data stacks.