Job description

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts. Job Category Software Engineering Job Details About Salesforce Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we re looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce s core values at the heart of it all. Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce. Agents are changing how people build software and analyze data. Slack’s data platform needs to support both agents that surface insights, and developer tools that use data to provide quantitative feedback in the product iteration process. We’re looking for a Staff or Senior Staff engineer to help build out Agentic Analytics at Slack. You’ll collaborate on technical direction, build, and go from early prototypes through production. Your work could span many parts of the data stack: ingestion, infrastructure, semantic layers, and the interfaces that make data useful to humans and AI agents. You’ll partner with engineers across Data Engineering, Data Science, product, core infrastructure, and developer experience to quickly iterate on high leverage problem spaces and ship pragmatic solutions. You’ll help us : Build data platform for agents. Design APIs, MCPs, and tools that let agents map data concepts to the right entities in the data warehouse. Make data digestible. Improve our semantic layer so people and agents alike can navigate metric definitions, lineage, ownership, and reliability. Make data on time (or provide signal when it’s not). Build ingestion and infrastructure for timely analytics, and experimentation. Evaluate tradeoffs between freshness, accuracy, reliability, and cost. Ship tools that make data easy to analyze. Make it easy to draft accurate queries, do root cause analysis, and build visualizations. Own the path to production. Drive architecture, implementation, rollout, and adoption. Raise the engineering bar across teams by helping engineers make good design decisions, resolve technical dependencies, and grow through mentorship and leading by example. How you work matters : You’re a high velocity engineer. You decompose problem spaces into digestible chunks and constantly iterate. You’re biased toward action. You love to prototype to test ideas and get feedback early, but are okay throwing away bad ideas. You can quickly build context in unfamiliar areas. You can move between a pipeline, a service, or a developer tool, build enough context to contribute. You also know when to ask for help when it speeds things up. You balance speed and judgment. You can get a prototype off the ground quickly, but also build a reliable and scalable production system Minimum qualifications : 8 years of experience building and operating a data platform A track record of leading complex technical projects across teams as an individual contributor Strong software engineering skills balanced with deep data expertise Clear technical communication and the ability to build alignment with engineers, data scientists, analysts, and product partners Strong proficiency in programming languages like Python, Go, Java, or similar Bonus points for : Experience building infrastructure or tools for AI agents Experience with semantic layers, metrics platforms, data catalogs, or lineage. These might include familiarity with tools like Airflow, dbt, Databricks, sqlmesh, Datahub, Superset or Snowflake Expertise in SQL & Python to build data pipelines Experience building APIs, SDKs, or self-service platforms that other engineers use. Practical experience using AI development tools to accelerate implementation while maintaining engineering quality. Technologies We Use: Data: Trino, Spark, Pinot, Hive, Airflow, Iceberg, Spark, Datahub, Kafka Storage: S3 Infrastructure: Terraform, AWS Languages: Python, Go, SQL, Scala, Typescript Unleash Your Potential When you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best , and our AI agents accelerate your impact so you can do your best . Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future — but to redefine what’s possible — for yourself, for AI, and the world. Accommodations If you need a reasonable accommodation during the application or the recruiting process, please submit a request via this Accommodations Request Form . Please note that Salesforce uses artificial intelligence (AI) tools to help our recruiters assess and evaluate candidates’ resumes and qualifications throughout the recruiting process. Humans will always make any candidate selection and hiring decisions. Please see our Candidate Privacy Statement for more information about how we use your personal data and your rights, including with regard to use of AI tools and opt out options. Posting Statement Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that’s inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications – without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education. In the United States, compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. Salesforce offers a variety of benefits to help you live well including: time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program. More details about company benefits can be found at the following link: salesforcebenefits.com.pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records. At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions. The typical base salary range for this position is $197,300 - $365,200 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $237,700 - $401,400 annually. The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.

Requirements

Must-haves

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.
Job Category
Software Engineering
Build data platform for agents. Design APIs, MCPs, and tools that let agents map data concepts to the right entities in the data warehouse.
Make data digestible. Improve our semantic layer so people and agents alike can navigate metric definitions, lineage, ownership, and reliability.
Make data on time (or provide signal when it’s not). Build ingestion and infrastructure for timely analytics, and experimentation. Evaluate tradeoffs between freshness, accuracy, reliability, and cost.
Ship tools that make data easy to analyze. Make it easy to draft accurate queries, do root cause analysis, and build visualizations.
Own the path to production. Drive architecture, implementation, rollout, and adoption.
Raise the engineering bar across teams by helping engineers make good design decisions, resolve technical dependencies, and grow through mentorship and leading by example.
You’re a high velocity engineer. You decompose problem spaces into digestible chunks and constantly iterate.
You’re biased toward action. You love to prototype to test ideas and get feedback early, but are okay throwing away bad ideas.
You can quickly build context in unfamiliar areas. You can move between a pipeline, a service, or a developer tool, build enough context to contribute. You also know when to ask for help when it speeds things up.
You balance speed and judgment. You can get a prototype off the ground quickly, but also build a reliable and scalable production system
8 years of experience building and operating a data platform
A track record of leading complex technical projects across teams as an individual contributor
Strong software engineering skills balanced with deep data expertise
Clear technical communication and the ability to build alignment with engineers, data scientists, analysts, and product partners
Strong proficiency in programming languages like Python, Go, Java, or similar
Experience building infrastructure or tools for AI agents
Experience with semantic layers, metrics platforms, data catalogs, or lineage. These might include familiarity with tools like Airflow, dbt, Databricks, sqlmesh, Datahub, Superset or Snowflake
Expertise in SQL & Python to build data pipelines
Experience building APIs, SDKs, or self-service platforms that other engineers use.
Practical experience using AI development tools to accelerate implementation while maintaining engineering quality.
Data: Trino, Spark, Pinot, Hive, Airflow, Iceberg, Spark, Datahub, Kafka

Nice-to-haves

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Keywords to mirror

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Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Parental Leave