Divisions Maintenance Group (DMG) needs to accelerate its pace of turning raw data into revenue-driving insights. The company captures millions of data points daily and requires a Senior Revenue Intelligence Engineer to build foundational data that empowers the Revenue Strategy & Planning (RSP), Go-To-Market teams, and the entire organization to make faster, smarter decisions that directly generate incremental sales and margin growth.
Requirements
- 8+ years of full_time data_engineering experience delivering end_to_end solutions that support Sales, Marketing, or Finance.
- Expert_level SQL (minimum 5 years writing complex queries) and strong Python skills; hands_on experience with Airflow or Glue.
- Advanced experience with cloud data platforms - preferably Snowflake and Postgres - including ETL/ELT, data modeling, and performance tuning.
- Demonstrated success integrating Salesforce data and at least one ERP/billing system (NetSuite preferred); familiarity with CDC tools such as Fivetran or Airbyte is a plus.
- Experience publishing enterprise_grade data models into the data warehouse including DAX Measures and workspace governance.
- Solid understanding of data management best practices: ingesting new data, transforming/harmonizing it, and making it actionable for analytics.
- Familiarity with AWS Glue, PySpark, Scala, Kafka, or similar technologies is advantageous.
Responsibilities
- Work with the data teams to Architect and maintain a Customer & Revenue 360° data model that unifies Snowflake, Salesforce, NetSuite, Dialpad, HRIS, and external enrichment data to be consumed by the entire organization.
- Design and orchestrate efficient ELT pipelines in SQL and Python using tools such as dbt, Airflow and Glue to refresh critical tables in hours, not days.
- Publish certified datasets and semantic layers to Power BI, enabling self_service dashboards for pipeline health, contracted margin, and funnel velocity.
- Partner with RevOps, Sales, and Finance leaders to translate business needs into data roadmaps and actionable insights that guide territory design, pricing strategies, and resource allocation.
- Implement pragmatic data_quality, freshness, and lineage checks to ensure GTM metrics remain accurate and trusted.
- Document data contracts, definitions, and best practices; coach analysts and stakeholders on leveraging curated datasets and reducing ad_hoc engineering requests.
Other
- Proven ability to translate business concepts into a clear data roadmap and technical execution plan.
- Excellent communication and collaboration skills, with the ability to work across technical and non_technical teams in a fast_paced environment.
- Ability to manage the stress of a fast-paced environment.
- Ability to meet the in-person requirements of the team and/or business needs.
