Data Engineer

As a Data Engineer on the Finance team, you’ll lead the end-to-end design, implementation, and maintenance of Bloomerang’s enterprise data warehouse. This role blends traditional data engineering with data warehouse development, giving you ownership over how the organization’s internal data is structured, governed, and delivered. You’ll drive the migration and refactor of our current data warehouse from Google BigQuery to Databricks, powering all internal analytics across Bloomerang. You’ll also establish the data warehouse standards and guidelines that shape how every team builds on top of our data platform going forward. You’ll partner with cross-functional stakeholders across Finance, Sales, Marketing, Customer Success, and People to turn business questions into reliable, well-modeled data.

What You Will Do

• Lead the end-to-end design, development, and implementation of enterprise data warehouse solutions supporting corporate analytics
• Own the migration and refactor of Bloomerang’s data warehouse from Google BigQuery to Databricks, supporting all internal analytics use cases
• Establish and maintain data warehouse standards, guidelines, and best practices across the organization
• Design and build scalable ETL and ELT pipelines that ingest, transform, and integrate data from internal systems, including MySQL, PostgreSQL, Salesforce, and more
• Work closely with analytics engineers to develop dimensional data models and enterprise data architecture that support reporting and business intelligence needs across the company
• Partner with cross-functional stakeholders to translate business requirements into scalable, well-governed data solutions
• Build automated data quality monitoring, validation, and governance processes to ensure accuracy and reliability of enterprise data
• Optimize data warehouse performance, reliability, and cost across cloud environments
• Create and maintain documentation for data architecture, pipelines, and data warehouse standards
• Mentor team members and collaborate with engineering on data warehouse best practices

What You Need to Succeed

• 3-5 years of experience in data engineering or data warehouse development, ideally with experience leading enterprise data warehouse implementations
• Hands-on experience migrating or working across modern cloud data warehouse platforms (e.g., BigQuery, Snowflake, Redshift); Databricks experience preferred
• Expert-level SQL skills and strong data modeling capabilities for complex querying, transformation, and enterprise reporting
• Experience with modern ETL and ELT tools and practices
• Strong understanding of dimensional modeling, data warehouse design patterns (star and snowflake schema), and enterprise data architecture
• Experience establishing data governance, standards, and best practices for agrowing organization
• Proficiency in Python or similar programming languages for data pipelinedevelopment
• Experience integrating with core business systems and APIs, including Salesforce,MySQL, and PostgreSQL
• Familiarity with cloud platforms (preferably Google Cloud Platform) and moderndata stack tools
• Experience with version control, CI/CD practices, and collaborative developmentworkflows
• Strong analytical and problem-solving skills with close attention to data qualityand accuracy
• Excellent communication skills and the ability to lead cross-functional collaboration in a fast-paced, growing organization
• Experience working in a SaaS or technology company is a plus, but not required