Staff Software Engineer - Semantic Foundation

WEXWEXUS•San Jose, CA

7d•$140,600 - $173,100•Remote

About The Position

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. We are seeking a Staff Software Engineer (Semantic Foundationa) with 6+ years of hands-on experience to join this team as a core platform engineer and DevOps specialist. In this role, you will take direct ownership of our orchestration and containerization stack while driving key initiatives in CI/CD automation, observability, data governance, and cloud cost optimization. You will bridge the gap between Data Engineering, Infrastructure, and Operations.

Requirements

6+ years of hands-on professional experience in Data Platform Engineering, DevOps, or Site Reliability Engineering (SRE) supporting big data environments.
Deep production experience managing, tuning, dynamic scaling, and troubleshooting Apache Airflow infrastructure (Celery/Kubernetes Executors, DAG parsing performance, dynamic configurations).
Expert-level skills in Kubernetes, Helm, Terraform, Docker, ArgoCD, and GitHub Actions (including custom runner configurations).
Proven track record of configuring production alerting, metrics collection, and log aggregation using Grafana, Prometheus, and Loki.
Deep operational and configuration experience with Spark, AWS EMR, Snowflake, Azure Synapse, dbt, and real-time streaming via Apache Kafka.
Solid hands-on experience with AWS (EC2, S3, VPC, IAM, EKS) and/or Azure, with a demonstrated history of driving cloud cost optimization.
Experience deploying or managing data cataloging tools like DataHub, Amundsen, or similar metadata management platforms.
Strong programming skills in Python, Bash, Go, or SQL.
Comfortable taking complex architectural requirements from concept to production-grade deployment in a high-paced environment.
Driven to replace manual operational tasks with code, automated tests, automated CI/CD checks, and resilient self-healing infrastructure.

Responsibilities

Design, deploy, scale, and maintain highly available Apache Airflow clusters (using Helm, Kubernetes, and Terraform) to support critical enterprise ETL/ELT workflows.
Drive DevOps practices using Terraform, Helm Charts, and ArgoCD to automate platform deployments, manage self-hosted GitHub runners, and enforce GitOps workflows.
Architect and manage robust CI/CD pipelines utilizing GitHub Actions for seamless deployment of data pipelines, infrastructure components, and DAGs.
Provision and manage scalable Kubernetes (EKS/AKS) clusters, Docker containers, and underlying cloud infrastructure across AWS (EC2, EMR, S3, VPC) and Azure (Synapse, ADLS).
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.
Build scalable data infrastructure supporting big data processing engines and data warehouses, including Apache Spark, AWS EMR, Snowflake, Azure Synapse, Apache Kafka, and dbt.
Deploy and maintain central data discovery and metadata tooling (e.g., DataHub) to facilitate data governance, schema management, and cataloging.
Actively monitor, audit, and optimize data infrastructure compute and storage costs across AWS and Azure (EC2, EMR, Snowflake queries, Kubernetes nodes).
Build internal tools, CLI utilities, and dynamic workflow templates to improve developer productivity for data engineers, analytics engineers, and data scientists.
Take full technical ownership of data platform modules from architectural design through deployment, production operations, and incident management.
Define and enforce high engineering standards for code quality, design patterns, testing, data lineage, and security (access controls, IAM, dynamic schema management).
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.
Serve as a subject matter expert (SME) on data infrastructure, guiding and mentoring junior and mid-level data platform engineers.