Data Engineer

 Key Responsibilities

• Build and maintain data pipelines across CRM, billing, and accounting systems
• Ensure data reconciliation, consistency, and accuracy across multiple enterprise platforms
• Design and maintain core data models for revenue, customer, and financial datasets
• Build and optimize semantic data layers to unify data across disparate systems
• Implement data quality checks, monitoring, and validation frameworks
• Configure, maintain, and optimize ETL/ELT pipelines for multiple data sources
• Support analytics and AI data foundations to enable downstream reporting and automation use cases
• Provide administration and support for enterprise BI tools and reporting platforms
• Collaborate with cross-functional teams to ensure data availability, integrity, and usability

Requirements

• Bachelor’s degree in Data Science, Computer Science, Engineering, Big Data, or related field (Master’s preferred)
• 3–6+ years of experience in Data Engineering or Analytics Engineering roles
• Strong expertise in SQL and data modeling (star/snowflake schemas, dimensional modeling)
• Experience building and maintaining data pipelines (ETL/ELT workflows)
• Hands-on experience with cloud data warehouse/lake platforms (e.g., Snowflake, BigQuery, Redshift, Databricks)
• Experience working with finance or revenue systems data (billing, accounting, ERP systems such as Oracle DB or similar)
• Experience integrating and working with CRM data systems (Salesforce preferred)
• Strong understanding of data governance, validation, and quality frameworks
• Ability to work in cross-functional, remote or distributed teams
• Strong problem-solving skills with attention to data accuracy and reliability
• Ability to communicate technical concepts to non-technical stakeholders

Preferred Skills

• Exposure to BI tools (Power BI, Tableau, Looker, etc.) administration and support
• Familiarity with data orchestration tools (Airflow, dbt, Prefect, or similar)
• Understanding of data lakehouse architectures and modern data stacks
• Exposure to machine learning data pipelines or AI/ML readiness frameworks
• Experience supporting revenue operations or finance analytics use cases
• Knowledge of data observability and monitoring tools

AI Competency Expectations

• Understanding of how data pipelines support AI/ML models and predictive analytics
• Experience enabling AI-ready datasets for automation or analytics use cases
  • Familiarity with embedding structured data for AI consumption (semantic layers, feature stores)