Senior Software Engineer, Salesforce

• Engineering Roots: Strong background in software engineering (Python/Java/Go) applied to data. You are comfortable writing custom API integrations and complex Python scripts
• The Modern Stack: Deep production experience with Snowflake (architecture/tuning) and dbt (Jinja/Macros/Modeling)
• Workflow Orchestration: Advanced proficiency with Airflow (Managed Workflows for Apache Airflow)
• Cloud Native: Hands-on experience with AWS services (S3, Lambda, IAM, ECS) and containerization (Docker/Kubernetes)
• DevOps Mindset: Experience with Git, CI/CD (GitHub Actions/Jenkins), and Terraform
• 5+ years of relevant data or software engineering experience

Desirable

• We are looking for exceptional Senior Engineers to build the engine that powers Salesforce’s enterprise intelligence
• In this role, you will be a hands-on technical contributor responsible for modernizing our core data ecosystem
• You will move beyond simple ETL scripts to build a robust, software-defined Data Mesh using Snowflake, dbt, Airflow, and Informatica
• You will bridge the gap between "Data Engineering" and "Software Engineering"—treating data pipelines as production code, automating infrastructure with Terraform, and optimizing high-scale distributed systems to enable AI and Analytics across the enterprise
• Build & Ship: Design and implement scalable data pipelines and transformation logic using Snowflake (SQL) and dbt. Replace legacy hardcoded scripts with modular, testable, and reusable data components
• Orchestration: Engineer robust workflows in Airflow. Write custom Python operators and ensure DAGs are dynamic, factory-generated, and resilient to failure
• Performance Tuning: Own the performance of your datasets. Deep dive into query profiles, optimize pruning/clustering in Snowflake, and reduce credit consumption while improving data freshness
• Infrastructure as Code: Manage the underlying platform infrastructure (warehouses, roles, storage integration) using Terraform or Helm. Click-ops is not an option
• CI/CD & Quality: Enforce a strict "DataOps" culture. Ensure every PR has unit tests, schema validation, and automated deployment pipelines
• Reliability (SRE): Build monitoring and alerting (Monte Carlo, Grafana, Newrelic, Splunk) to detect data anomalies before stakeholders do
• Data Mesh Implementation: Work with domain teams (Sales, Marketing, Finance) to onboard them to the platform, helping them decentralize their data ownership while adhering to platform standards
• AI Readiness: Prepare structured data for AI consumption, ensuring high-quality, governed datasets are available for LLM agents and advanced analytics models
• Focus: Execution & Component Ownership. You are given a problem (e.g., "Migrate this domain to dbt," "Optimize this slow pipeline") and you solve it with high-quality, clean code with minimal supervision
• Scope: You own features and specific pipelines. You mentor junior engineers on code reviews and best practices