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Principal Database Infrastructure Engineer 📍 Remote, United States   |    Remote    |   $190,000 to $220,000 + Equity + Benefits   About VideoAmp   VideoAmp is the tech-first measurement company transforming how advertising is valued, bought, and sold. Powered by currency-grade big data and a best-in-class technology stack, our platform gives advertisers, agencies, and digital partners the ability to plan, optimize, and measure media investments across every screen, from linear TV and OTT to CTV and digital video. VideoAmp is accelerating investment in agentic AI and intelligent optimization technologies, helping clients drive measurable, real-world outcomes in an increasingly complex media landscape. With 880% year-over-year measurement growth, 98% coverage of the TV ecosystem, and partnerships with 11 agency groups and 1,000+ advertisers, we're not just keeping pace with the industry. We're defining what comes next. We believe great work requires great people, people who say "I'll find a way" instead of "it can't be done."   The Role   The Principal Database Infrastructure Engineer will serve as a technical cornerstone of VideoAmp's Database Infrastructure team, driving the design and execution of scalable, production-critical data systems that power VideoAmp's platform. This is a high-impact individual contributor role at the intersection of distributed database engineering, query performance, storage architecture, and developer enablement. You will architect and own the foundational database systems serving live customers and internal teams, operate in a rigorous, reliability-driven culture, and help VideoAmp scale its data infrastructure as the platform grows.   What You'll Do   Distributed Query Execution Design and implement the physical plan distribution pass, including network shuffle, coalesce, and partition isolator insertion. Own the plan serialization codec that ships sub-plans to workers, and maintain the S3 and Flight result exchange paths.   Worker Coordination at Scale Own worker discovery and heartbeating, and lead development of the next generation of load balancing: a work-stealing protocol and a replication-aware hash ring, both currently in design, to keep workers evenly loaded and resilient to node loss.   Query Optimizer Internals Work across cost-based join reordering, cross-stage bloom filter cascade, scan deduplication, and selectivity estimation. Several of these live in our DataFusion fork; you will upstream where it makes sense and maintain the delta where it does not.   Storage and Caching Own the NVMe LRU cache over S3, Parquet read strategies including full-file and range reads, and Iceberg partition pruning and snapshot handling.   Performance Engineering Close the remaining gap on queries where we still trail Snowflake, specifically multi-shuffle plans and redistribution after scalar-subquery extraction. TPC-H benchmarks are the scorecard.   What You'll Bring   Required 8+ years of software engineering experience with significant depth in database infrastructure, distributed systems, or data platform engineering. Strong systems programming in Rust, or deep C++ or Go experience with a clear path to Rust, including async runtimes such as Tokio and concurrent data structures. Proven experience building or significantly modifying a distributed data system such as a query engine, stream processor, distributed database, or large-scale data pipeline, with a solid understanding of shuffles, partitioning, and network and memory bottlenecks. Fluency with columnar formats and vectorized execution, including Arrow, Parquet, and the mechanics behind their performance characteristics. Strong grounding in distributed systems fundamentals: consistent hashing, leader and heartbeat protocols, backpressu