Senior Software Engineer - Data Platform

Why Glassdoor?

When you work for Glassdoor, you help people everywhere get ahead in work and life. That’s because we’re moving fast to become the leading community for workplace conversations. Every day, we foster radical transparency by giving professionals a platform to connect authentically and anonymously. Think real talk with real people — plus company reviews and ratings, salary info, job listings and more. 

Join us as we make worklife better, together.

About The Role

It is a transformational time at Glassdoor. As we introduce our community offering to the world our data systems must scale to support the significant increase in engagement with the Glassdoor and Fishbowl products. Therefore, our data platform is also going through a transformational shift that sees the re-architecture and design of every piece of infrastructure from stream and batch processing to our data lake and overall data strategy. The Senior Software Engineer that joins the team will be incredibly valuable in helping us build out the next generation data platform that will support Glassdoor and Fishbowl’s many products. This role will report into the Engineering Manager of Data Platform.

What You’ll Do

• Introduce best practices in software development across data engineering and machine learning teams, ensuring technical architecture and design decisions support scalability, performance, and maintainability.
• You'll champion a culture of quality, continuous improvement, and technical mentorship within the team while partnering closely with various other teams across the engineering organization.
• You’ll set and enforce best practices and standards for the data platform team and ensure that our platform runs smoothly while keeping our cloud environment clean.
• You’ll mentor junior engineers, challenging their technical skills to help them grow into well-rounded engineers.
• You’ll conduct regular code and architecture reviews for the data platform team championing new approaches and refining older ones to keep us at the cutting edge of technology.
• Introduce a robust data quality strategy aimed to eradicate poor data from making its way into production.

What You’ll Bring

• 4+ years of experience in software, platform, DataOps, MLOps engineering or a similar role.
• Strong data product and business sense to drive decision making that allows you to put yourself in the shoes of the end-user.
• Strong interpersonal and collaboration skills, with the ability to work effectively across functions and influence decision-making.
• Experience in stakeholder management and building consensus among diverse groups.
• A team player outlook, with a growth mindset and an impact-driven approach 
• Expertise in container and container orchestration tools (Docker and/or Kubernetes)
  • K8s cluster management
  • Performance optimizations
  • Updating and deploying helm charts
• Expertise with CI/CD (GitHub Actions, GitLab CI, Jenkins, etc) fundamentals and implementation for big data tools
• Strong AWS cloud fundamentals:
  • Experience with IaC (CloudFormation and/or Terraform)
  • EMR, S3, EC2, EKS, ECS, ECR, VPC, IAM, Route 53, Kinesis, Lambda, Glue, and more
• Hadoop fundamentals:
  • HDFS, Hive, Tez, Spark, and more
• Strong DevOps and SysOps experience
  • Networking experience (VPC, network peering, TCP/IP, subnets, etc)
  • Monitoring, observability, and alerting with DataDog, CloudWatch, and/or Grafana
• Strong software fundamentals:
  • Hands-on development & writing code (Python, Java, Scala, etc)
  • Unit testing, mocking, and patching strategies (pytest, unittest, mockito, etc)
  • OOP/OOD and software design patterns (factory, facade, builder, adapter, etc)
  • Experience with UML diagrams
• Strong data fundamentals:
  • Development of custom Airflow operators and libraries
  • Maintaining Airflow webserver, scheduler, and metastore
  • Maintaining EMR clusters for Hive and Spark workloads
  • Snowflake fundamentals
  • Streaming data with Kafka and transformations with ksqlDB/Flink
  • Experience with TimescaleDB, ClickHouse, and/or Snowflake
  • Understanding fundamentals of data architecture and modeling
• Strong AIOps experience:
  • Understanding of ML Lifecycle
  • Understanding of agentic design
  • Proficiency in managing GPU instances, managing and monitoring GPUs in cluster environments
  • GPU concurrency & time-slicing
  • Experience working with tools like MLFlow, Kubeflow, KServe or BentoML
  • ML model registries, DVC, etc
• Exper