Senior Data Infra Engineer

Founded in 2015, NewsBreak is the Content Intelligence platform shaping the future content economy. With over 40 million monthly active users, our flagship platform delivers highly personalized local news and information powered by advanced AI, recommendation systems, and adtech.

Recognized by Fast Company as #32 on the Top Workplaces for Innovators, we're proud to be Great Place to Work® certified and home to a dynamic team of technologists, product innovators, and business leaders who are passionate about solving meaningful challenges at scale.

Together, we reached unicorn status in 2021, and we remain committed to continuing this high-growth trajectory with the right team to fulfill our mission: building the infrastructure layer for content intelligence.

If you’re inspired to dream big, innovate fast, and make a difference, we’d love to hear from you! For more information, visit www.newsbreak.com/about

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.