Senior or Lead Data Engineer, Salesforce

• The right candidate will have strong data architecture, ETL/ELT, SQL, and a proven track record of working with enterprise metrics to build automated data pipelines
• The candidate will also have strong operational skills to drive efficiency and speed, expertise in building repeatable data engineering processes, strong project management skills, and a vision for how to deliver data products
• 7+ years of related information systems experience in data engineering, data modeling, automation, and analytics
• Deep understanding of data engineering concepts, database designs, associated tools, system components, internal processes, and architecture
• Well-versed with Snowflake, Google BigQuery, and Redshift
• Experience with ETL/ELT technologies like DBT, IICS, or Fivetran
• Proficiency with SQL, Bash, and Python scripting
• Hands-on experience with Airflow, CI/CD pipelines via Jenkins or similar tools, and GitHub
• Good experience with AWS technologies like ECS, CloudWatch, and Lambda
• Experience working as a mentor to junior members in a customer-focused team
• Experience working closely with Analytics/Data Science groups
• Team-first mentality with excellent interpersonal skills to build strong relationships crucial for success in this role
• Must be able to proactively communicate status and identify risks
• Self-starter, highly motivated, able to shift directions quickly when priorities change, think through problems to come up with innovative solutions, and deliver against timelines
• Must be results-oriented and able to move forward without complete information and with minimal direction
• Experience working with globally distributed engineering teams preferred
• A related technical degree is required

Desirable

• Salesforce is embarking on our own Digital Transformation to deliver customer success for our customers and accelerate our growth
• A key pillar of this transformation is building a strong data foundation to support all of our core marketing needs through intelligent data sets as a service
• We’re looking for an experienced and committed data engineer to build data pipelines and metrics for the sales and marketing organization
• You will work with business partners across marketing functions to understand business needs, translate them to technical requirements, wrangle data from various systems, and design automated data pipelines to drive insights
• You will collaborate with internal technology groups to automate the data collection and definition process
• Actively develop, enhance, and maintain data pipelines and workflows for marketing data and metrics
• Work with marketing teams across the organization to build data metric requirements, create quick POCs, and document functional and technical requirements
• Develop the framework in Snowflake and BigQuery to automate the ETL process
• Develop DBT ETL pipelines for data ingestion and transformation
• Maintain, deploy, and version the ETL process code
• Use GIT CI/CD for DevOps
• Work with business partners to analyze metrics and build data insights
• Help in building POC solutions for new marketing metrics that drive effective decision-making
• Design and develop easy, repeatable, and reusable automation data frameworks
• Responsible for end-to-end data management activities, including identifying fields, data lineage and integration, performing data quality checks, analysis, and presenting data
• Manage business intelligence and data warehouse plans across marketing
• Review and validate logical and physical designs to ensure alignment with the defined solution architecture. Review solution designs to ensure that the defined standards and framework are followed
• Evaluate, determine root cause, and resolve critical issues
• Work with internal team members and external partners to support data collection and analysis and understand reporting needs
• Collaborate with Data Science teams and support them with all the data they need to produce their models
• Work and collaborate with global teams across North America, EMEA, and APAC