Senior Data Engineer

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.

We’re seeking a Senior Data Engineer to join our Data Engineering team. In this role, you’ll design and build scalable data solutions that power machine learning, analytics, and data products across our portfolio of companies—including Glassdoor, Fishbowl, Indeed, and Recruit.

The ideal candidate has deep software engineering fundamentals, hands-on experience with cloud and big data technologies, and a strong track record of building robust, scalable systems in production. You’re passionate about data, committed to quality, and excited about working in a fast-paced, collaborative environment.

We work at petabyte scale and embrace a wide range of technologies, offering unique opportunities to solve complex data challenges across multiple business domains. You’ll collaborate closely with platform engineers, data scientists, ML engineers, and business stakeholders to deliver end-to-end data solutions. While this is an individual contributor role, we value strong collaboration and cross-functional teamwork.

If you’re excited about building cutting-edge data problems and exploring the latest technologies, we’d love to hear from you.

For more information about some of the exciting technology work being done at Glassdoor, check out our Engineering Blog!

What You’ll Do

• Design, build, and maintain scalable batch and streaming data pipelines using technologies like Apache Airflow, Spark, Flink, Kafka, Iceberg, and Snowflake
• Develop real-time data workflows using engines such as Kafka or Kinesis
• Collaborate with cross-functional teams—product managers, software engineers, ML engineers, and data scientists—to design data models and pipelines that support business use cases
• Leverage AI tools to improve development velocity, data quality, and platform reliability
• Drive the evolution of our data platform, ensuring efficiency, resiliency, and scalability
• Apply software engineering best practices, including unit and integration testing, to data workflows
• Participate in a rotational on-call schedule to support production systems
• Maintain and enhance existing systems to meet evolving business needs
• Quickly ramp up on our technology and domain, and proactively share knowledge with the team

What You’ll Bring

• A team player outlook, with a growth mindset and an impact-driven approach
• 5+ years of experience developing scalable, resilient data engineering solutions
• 3+ years of hands-on experience with distributed data processing and cloud technologies (e.g., Spark, Flink, Kafka, Snowflake, Databricks, Redshift)
• Experience designing and modeling data for data lakes and modern data architectures (batch and streaming) at multi-terabyte or petabyte scale
• 4+ years of experience coding in Python and applying software engineering best practices
• Deep understanding of distributed data processing, data modeling, and building ETL/ELT pipelines
• Familiarity with data architecture patterns (Lambda vs. Kappa), OLTP vs. OLAP systems, and data modeling strategies
• Exposure to test-driven development and automated testing frameworks
• Experience working in Agile/Scrum environments
• Ability to manage multiple priorities with minimal supervision
• Strong communication and collaboration skills—able to work across functions and explain technical concepts to non-technical audiences
• Proven experience with modern data stack principles and tools
• Bachelor’s degree in Computer Science or equivalent professional experience
• A strong interest in data and building high-impact, scalable systems in a tech-forward, AI-enhanced environment
• Experience building customer-facing products, machine learning pipelines, or data products
• Familiarity with CI/CD pipelines, containerization (Docker, Kubernetes), and cloud platforms (AWS, GCP)
• Exposure to modern data tools like DBT, Soda Spark, Great Expectations, Anomalo, or Monte Carlo
• Experience with observability and alerting tools such as DataDog
• Contributions to open-source projects or active involvement in the data engineering community

Compensation and Benefits

Base Salary Rang