Senior Data Infra Engineer

Local life runs on local information. What happened down the street? Who can fix this? Where should we go tonight? NewsBreak started in 2015 to answer these questions, and today more than 40 million people a month rely on us to follow what’s going on in their zip code.

Three platforms with one vision: powering local life. Led by founder and CEO Jeff Zheng, NewsBreak is our local life and news platform; Nearby AI connects people with local businesses; and NDot AI helps publishers and creators grow and earn.

Building for the long term. A $115 million Series C made us a unicorn in 2021, and we keep investing in our people. We’re Great Place to Work® certified and named to Fast Company’s Best Workplaces for Innovators (2025) and Fortune’s Best Workplaces in the Bay Area™ (2026).

Everyone makes an impact. Whether you write code, shape products, tell our story, or run the business, we want people who own projects end to end, stay curious, and speak up. Join us.

Learn more at: NewsBreak, Nearby AI, NDot AI

NewsBreak reaches tens of millions of users every day. Every feed ranking decision, every recommendation, every A/B test, and every ML model we ship runs on the data infrastructure you'd own. We're looking for a senior engineer to build and scale the batch and streaming pipelines at the core of that platform — someone who takes end-to-end ownership, drives reliability, and can translate ambiguous business needs into production-grade data systems.

• →Own the streaming data backbone — design and operate high-throughput Kafka pipelines carrying user events (clicks, reads, impressions, shares) from mobile clients through to downstream consumers: the data warehouse and real-time analytics.
• →Build and maintain batch pipelines at scale — author and optimize Spark jobs processing billions of rows daily: content ingestion, engagement aggregation, and user-level feature computation. Own pipeline reliability, incremental backfill strategies, and cost per TB processed.
• →Drive pipeline observability — instrument data quality checks, freshness monitors, and anomaly alerts so issues are caught before they reach dashboards. Define SLOs for data freshness and completeness, and own them.
• →Model structured and unstructured data — design data models that serve analytics and product use cases. Act as the data POC across teams and be the person who pushes back when a schema decision will hurt us six months later.
• →Partner with data scientists and platform engineers — understand data needs across teams and drive key data infrastructure decisions. Unblock downstream teams without becoming a ticket queue.
• →Improve efficiency — reduce compute costs and job latency through query optimization, smarter partitioning, better resource scheduling, and tiered storage strategies.
• →Raise the platform bar — contribute to shared infrastructure: pipeline framework standards, orchestration patterns (Airflow), reusable Spark libraries, and data catalog hygiene. Mentor junior engineers on the team

• 5+ years of data engineering experience, with at least 3 years owning production pipelines in a distributed data environment.
• Strong hands-on experience with Spark and Kafka at scale — you've debugged a production incident in both, not just run tutorials.
• Experience with big-data technologies including Hadoop, Presto/Trino, and Flink.
• Proficiency in Python and SQL; Scala a plus.
• Experience working in a cloud environment, preferably AWS (S3, EMR, Glue, Redshift).
• Track record of improving efficiency, scalability, and stability of data systems — with measurable results.
• BS or MS in Computer Science or equivalent.
• Strong communication skills; comfortable driving data infrastructure decisions across application and platform teams.