Software Engineer, Big Data, Tech Enablement

About the role

We offer a hybrid work environment. Most US-based positions can also be performed remotely (any exceptions will be noted in the Minimum Qualifications below.)

Summary

The Tech Enablement team is central to the velocity and quality of our entire engineering organization. Our mission is to build the scalable, reliable, and efficient infrastructure, tools, and processes that allow every product team to quickly deliver innovative products and services to our customers.

The Tech Enablement team ensures engineering efficiency and provides the foundational building blocks for the entire technology stack:

• Data Infrastructure & Systems: We help developers at ZipRecruiter sustainably follow best practices and implement robust solutions by allowing engineers and data scientists to focus less on underlying technologies and more on building products and features that delight ZipRecruiter's customers. To do this, the team builds, maintains, and drives adoption of tools for engineers that aid in data processing, dataset ingestion, dataset discoverability, data pipeline orchestration, data documentation, data analysis, building ML model pipelines, and running experiments.
• AI Enablement: We help developers at ZipRecruiter leverage AI as a part of their tech stack by building and maintaining an agentic AI data exploration and analysis tool to democratize access to data, collaborate on insights, and accelerate data driven decision making. Starting from a natural language chat interface, the AI assisted tool assists with every aspect of using data, from finding the right data sources and writing queries to using them to visualize and analyze the results. We also manage an LLM Gateway which powers all of ZipRecruiter's LLM usage in our customer facing products.

As a Big Data Engineer in Tech Enablement, you will

• Data Ingestion & Datalake Management: Design, build, deploy, and maintain the data pipelines and systems that make web events available in our datalake and via event streams for various downstream applications to process.
• Big Data Processing: You'll tackle challenges with large, evolving datasets, using a mix of batch and streaming techniques to make critical trade-offs between speed, efficiency, and reliability.
• MLOps: Design, build, deploy, and maintain frameworks and systems that accelerate ML development across teams and products at ZipRecruiter, with the goals of improving the velocity, quality, observability, and reliability of ML development and ML powered products.
• Data Documentation Framework: Design, build, deploy, and maintain frameworks and systems to improve the comprehensiveness and quality of data documentation at ZipRecruiter through processes and tooling that allows both humans and automated processes to easily add, maintain, and discover documentation.
• Data Analysis Agent: Design, build, deploy, and maintain an agentic data exploration and analysis tool, Sift, to democratize access to data, collaborate on insights, and accelerate data driven decision making.

Minimum Qualifications

• 2+ years of professional software development experience, including building and operating data pipelines that feed into a customer facing product
• Hands-on Apache Spark experience: writing, tuning and debugging batch and/or streaming jobs at scale
• Experience deploying and operating containerized services and batch jobs
• Solid computer science fundamentals: data structures, algorithms, distributed systems, and testing
• Professional software development experience, including building and operating data pipelines and infrastructure that feed into a customer facing product

Preferred Qualifications

• 4+ years of professional software development experience with a focus on data-intensive systems
• Working knowledge of AWS (S3, IAM, EC2, MSK or equivalents) and of instrumenting jobs with metrics, logs and alerts
• Experience with a data lake stack: parquet on object storage, a data catalog such as AWS Glue, and an open table format such as Apache Iceberg or Delta Lake
• Streaming experience: Kafka producers and consumers, Apache Flink, and/or Spark Structured Streaming
• Experience with a workflow orchestrator such as Apache Airflow: authoring DAGs, backfills, dependency management and on-call for scheduled pipelines
• Experience with open table format internals: schema evolution, partitioning, and compaction
• Experience with deploying services on Kubernetes with Docker
• Experience building and maintaining a data platform that powers a user-facing product including infrastructure and tools for streaming and batch data ingestion, building ETL pipelines, data permissions management, and data discovery.
• BS/MS/PhD in Computer Science, Mathematics, Physics or a related field, or equivalent practical experience