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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Software Engineer - Semantic Foundation - **Company:** WEX Inc. - **Location:** San Francisco, CA, United States (Remote available) - **Experience:** Expert - **Salary:** $140,600.0 - $173,100.0 - **Contract:** Permanent contract - **Skills:** Airflow, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Automation of Tests, Microsoft Azure, Bash Shell, Big Data, Cloud Computing, Software Quality, Computer Programming, Continuous Integration, Directed Acyclic Graph (Directed Graphs), Data Discovery, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Warehousing, Software Design Patterns, DevOps, Github, Monitoring of Systems, Identity and Access Management, Python (Programming Language), Meta-Data Management, Octopus Deploy, Performance Tuning, Reliability Engineering, Prometheus, SQL Databases, Data Streaming, Data Logging, Scripting, Delivery Pipeline, Snowflake, Grafana, Apache Spark, Kubernetes Helm Charts, Multi-Cloud, Electronic Medical Records, Amazon Virtual Private Cloud (VPC), Kubernetes, Infrastructure Automation Frameworks, Data Lineage, Apache Kafka, Celery, Terraform, Azure Synapse Analytics, Data Pipelines, Amazon Elastic Mapreduce (EMR), Docker, Golang - **Published:** September 17, 2026 - **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/10019611?backUrl=%2Fcareer%2F10019611%2FStaff-Software-Engineer-Data-California-San-Francisco ## About the Role * 6+ years of hands-on professional experience in Data Platform Engineering, DevOps, or Site Reliability Engineering (SRE) supporting big data environments. * Airflow Subject Matter Expertise: Deep production experience managing, tuning, dynamic scaling, and troubleshooting Apache Airflow infrastructure (Celery/Kubernetes Executors, DAG parsing performance, dynamic configurations). * Container & Infrastructure Automation: Expert-level skills in Kubernetes, Helm, Terraform, Docker, ArgoCD, and GitHub Actions (including custom runner configurations). * Observability & Monitoring: Proven track record of configuring production alerting, metrics collection, and log aggregation using Grafana, Prometheus, and Loki. * Big Data & Analytics Tech Stack: Deep operational and configuration experience with Spark, AWS EMR, Snowflake, Azure Synapse, dbt, and real-time streaming via Apache Kafka. * Cloud & FinOps: Solid hands-on experience with AWS (EC2, S3, VPC, IAM, EKS) and/or Azure, with a demonstrated history of driving cloud cost optimization. * Governance Tooling: Experience deploying or managing data cataloging tools like DataHub, Amundsen, or similar metadata management platforms. * Programming & Scripting: Strong programming skills in Python, Bash, Go, or SQL. Mindset & Execution * High Ownership: Comfortable taking complex architectural requirements from concept to production-grade deployment in a high-paced environment. * Automation-First Philosophy: Driven to replace manual operational tasks with code, automated tests, automated CI/CD checks, and resilient self-healing infrastructure. ## Description The Data Platform Engineering Team acts as the backbone of our enterprise data architecture, bridging the gap between Data Engineering, Infrastructure, and Operations. Responsible for architecting, scaling, and maintaining multi-cloud infrastructure across AWS and Azure, the team takes direct ownership of core Apache Airflow orchestration, big data frameworks, and containerized environments to ensure a robust, production-grade platform., * Airflow Infrastructure Ownership: Design, deploy, scale, and maintain highly available Apache Airflow clusters (using Helm, Kubernetes, and Terraform) to support critical enterprise ETL/ELT workflows. * Infrastructure-as-Code & GitOps: Drive DevOps practices using Terraform, Helm Charts, and ArgoCD to automate platform deployments, manage self-hosted GitHub runners, and enforce GitOps workflows. * CI/CD & Automation: Architect and manage robust CI/CD pipelines utilizing GitHub Actions for seamless deployment of data pipelines, infrastructure components, and DAGs. * Container & Cluster Management: Provision and manage scalable Kubernetes (EKS/AKS) clusters, Docker containers, and underlying cloud infrastructure across AWS (EC2, EMR, S3, VPC) and Azure (Synapse, ADLS). * Observability, Telemetry & Alerting: Build and maintain end-to-end monitoring, logging, and alerting systems using Grafana, Prometheus, Loki, and centralized log management solutions to ensure high platform uptime and reliability. Central Data Platform, Tools & Governance * Big Data Platform Architecture: Build scalable data infrastructure supporting big data processing engines and data warehouses, including Apache Spark, AWS EMR, Snowflake, Azure Synapse, Apache Kafka, and dbt. * Data Lineage & Governance: Deploy and maintain central data discovery and metadata tooling (e.g., DataHub) to facilitate data governance, schema management, and cataloging. * Cost Optimization & FinOps: Actively monitor, audit, and optimize data infrastructure compute and storage costs across AWS and Azure (EC2, EMR, Snowflake queries, Kubernetes nodes). * Developer Experience & Tooling: Build internal tools, CLI utilities, and dynamic workflow templates to improve developer productivity for data engineers, analytics engineers, and data scientists. Technical Leadership & Ownership * Architecture & End-to-End Ownership: Take full technical ownership of data platform modules from architectural design through deployment, production operations, and incident management. * Technical Excellence & Best Practices: Define and enforce high engineering standards for code quality, design patterns, testing, data lineage, and security (access controls, IAM, dynamic schema management). * Strategic Roadmap: Partner with data leads, product managers, and business stakeholders to identify infrastructure gaps, define a 1-2 year data platform roadmap, and prioritize platform initiatives. * Mentorship: Serve as a subject matter expert (SME) on data infrastructure, guiding and mentoring junior and mid-level data platform engineers.