Description
The Heroku team at Salesforce is seeking a Senior or Lead Data Engineer to design and scale automated data pipelines on AWS (Redshift, Kinesis) to power insights for Salesforce Data Cloud. You will be responsible for the end-to-end data lifecycle—from real-time ingestion via APIs and Eventbus to complex ETL processing and data modeling—ensuring high-quality data is available for Analytics and Data Science teams. By bridging the gap between business needs and technical architecture, you will play a critical role in automating data collection and delivering actionable recommendations for Salesforce’s enterprise and online business.
The ideal candidate brings 6+ years of experience with a mastery of SQL (PostgreSQL), Python, and orchestration tools like Airflow or Dagster. You should be a self-starter comfortable with ambiguity and possess the interpersonal skills to collaborate across internal tech teams. Experience with Salesforce Data Cloud, Heroku, or major ETL platforms like MuleSoft is highly preferred. Ultimately, this role is for a high-performing engineer who can deliver innovative, scalable solutions under tight deadlines while maintaining a team-first mentality.
Salesforce is on a mission to accelerate our own Digital Transformation to empower our customers and fuel our growth. A key part of this transformation is building robust data platforms and automated data pipelines to deliver data-driven insights and recommendations, especially for our Salesforce enterprise and online business, to empower growth of the platform.
The Heroku team is looking for a Data Engineer with experience building data pipelines and metrics for Sales or Marketing teams. You'll be a vital part of our team, helping us understand business needs and translating them into technical requirements. You'll work with data from various systems, design automated data pipelines, and drive insights for the Salesforce Data Cloud product. You'll also partner with internal tech teams to automate the data collection and definition process.
Your Impact:
Design and own efficient, scalable data pipelines on AWS for collecting, transforming, and loading data from various sources.
Lead end-to-end data management, including identifying key fields, data lineage, and integration.
Perform data quality checks and present data insights on the Salesforce Data Cloud platform.
Collaborate closely with Analytics and Data Science teams to meet their needs, using tools like Tableau to present data.
Proactively communicate project status and identify potential risks.
Required Skills:
6+ years in a data engineering, data modeling, or analytics role.
Deep understanding of data engineering concepts, database designs, associated tools, and system architecture.
Hands-on experience with cloud services like AWS, specifically Amazon Redshift and Kinesis.
Proficiency with PostgreSQL for database management and a strong command of SQL scripting and querying.
Strong experience with complex ETL processes (MuleSoft, Jitterbit, Informatica, etc.).
Proficiency with Python
Experience with orchestration tools like Apache Airflow or Dagster.
Proficiency with API-based and Eventbus real-time data ingestion.
Preferred Skills:
Experience with Salesforce Data Cloud.
Familiarity with the Heroku and Salesforce platforms.
Familiarity with Ruby
Who You Are:
You have a team-first mentality and excellent interpersonal skills to build strong, collaborative relationships.
You are a self-starter who is highly motivated and can quickly adapt when priorities change. You're comfortable moving forward without complete information and with minimal supervision.
You think through problems to come up with innovative solutions and can deliver results under tight deadlines.
You are focused on outcomes and driven to achieve goals.