Data Engineer

The Associate Data Engineer supporting Digital, Marketing, and Pro CX at Pella is responsible for building and maintaining the data foundations that enable scalable, reliable analytics across customer experience, digital performance, and marketing functions. This role focuses on designing data pipelines, transforming and modeling data, and ensuring high-quality, well-governed data is available for analytics, reporting, and advanced use cases.

This individual will work cross-functionally with the Digital team, Marketing team, and Pro CX team to support a wide range of analytics needs, including digital performance measurement, customer journey analysis, and marketing effectiveness. While not embedded within a single function, the role plays a critical part in enabling these teams by delivering trusted, production-ready data that supports insight generation and decision-making across Pella.

• Design, build, and maintain reliable and scalable data pipelines using Azure-native services and Databricks to ingest data from digital, marketing, and enterprise systems.
• Develop and manage analytics-ready data models using lakehouse and dimensional modeling patterns that support reporting, dashboards, and downstream analytics use cases.
• Implement data transformations, validations, and quality checks within Databricks to ensure data is accurate, consistent, and production ready.
• Optimize data pipelines and models for performance, cost, reliability, and reusability across the analytics ecosystem.
• Enable analytics and BI use cases by making curated data easily consumable in Power BI and other analytics tools.
• Partner closely with analysts, data scientists, and business stakeholders to translate analytical requirements into scalable technical solutions.
• Collaborate with platform, integration, and vendor teams to onboard new data sources and evolve existing pipelines.
• Document data pipelines, models, definitions, lineage, and assumptions to support governance, discoverability, and reuse.
• Contribute to the continuous improvement of analytics standards, engineering best practices, and architecture patterns.
• Support Digital, Marketing, and Pro CX teams by aligning analytics data with customer journeys, digital tools, and measurement frameworks.

• 3+ years of experience in data engineering, analytics engineering, or a related technical analytics role.
• Strong experience building data pipelines and transformations using Databricks (or comparable cloud-based data engineering platforms).
• Hands‑on experience with Microsoft Azure data services such as Azure Data Lake Storage, Azure Synapse, or similar.
• Strong SQL skills, including complex joins, aggregations, window functions, and query optimization.
• Proficiency in Python for data transformation, automation, and pipeline development.
• Experience designing analytics data models (e.g., star schemas, fact and dimension tables).
• Experience supporting BI and analytics tools, with a strong preference for Power BI.
• Familiarity with digital and marketing data sources such as web analytics platforms, CRM systems, and advertising platforms.
• Ability to collaborate effectively with analysts, data scientists, and business partners.
• Strong communication skills with the ability to explain technical concepts to both technical and non-technical audiences.

• Experience with:
• Databricks lakehouse architectures, Delta Lake, and notebook‑based development.
• Power BI semantic models, performance optimization, and enterprise reporting patterns.
• Data governance, metadata management, and documentation practices.
• Google Cloud Platform technologies such as Big Query, Google Analytics, or Google Tag Manager.
• Integrating data from platforms such as Salesforce, Fullstory, or other customer and digital experience tools.
• Supporting cross‑functional teams such as Digital, Marketing, Customer Experience, or E‑commerce.
• Modern CI/CD or version control practices for data pipelines and analytics assets.

• Experience working in hybrid‑cloud or multi‑platform analytics environments.
• Familiarity with data quality frameworks, monitoring, and observability.
• Interest in contributing to evolving enterprise analytics strategy and standards.