Engineering Manager, Data Platform

Engineering Manager For Data Platform TeamAs Engineering Manager for the Data Platform team, you'll lead a team of engineers building and operating the infrastructure that powers data ingestion, governance, storage, processing, and access across FloQast's product and analytics systems. This is a deeply technical role — roughly half your time is writing code, designing systems, and making architecture calls alongside your team. The other half is growing the people doing that work: hiring, developing, unblocking, and setting direction.You've spent years in data infrastructure and you still love the work. You can reason through a Spark shuffle problem, pick apart a slow Iceberg compaction job, and make a principled call between PySpark and Scala Spark on a real workload. You've run Structured Streaming pipelines, operated Kafka at scale, and debugged the Spark UI until the stage times made sense. But you've also learned that technical impact compounds through people — that the systems you architect matter less than the engineers you develop. You've managed before, or you've been the informal lead that everyone went to, and you're ready to own it fully.At FloQast, you'll apply that combination across our full lakehouse stack. FloLake runs on Iceberg over S3, orchestrated through MWAA, cataloged in AWS Glue, and queried via Trino and Athena. Kafka on MSK moves data through the hot path. The platform serves 30,000+ tenants. You'll hold technical authority on data compute and architecture decisions while building the team that executes them — and you'll do both at the same time, not in phases.What You'll DoAs a technical contributor (?50%)Write production code and lead architectural design on the data platform — pipelines, storage layers, access APIs, and observability infrastructureOwn technical decisions on Spark job structure, Iceberg table management, and streaming architecture on MSK/KafkaReview code at a level that raises the bar, not just catches bugs — you shape how the team thinks about distributed systemsDrive modernization and tooling choices (Snowflake, dbt, Airflow, Trino, and what comes next) with a bias toward operational simplicity and long-term maintainabilityContribute to data governance: quality frameworks, lineage, access control, and compliance (GDPR, SOC2)As a team lead (?50%)Work shoulder-to-shoulder with engineers on the hardest problems — you're in the design docs, the code reviews, and the incident calls, not observing from the sideGive engineers real feedback in the moment: on a PR, in a design review, after a demo — not just in quarterly check-insSpot where an engineer is stuck or growing and act on it directly, whether that means pairing with them, reshaping their scope, or getting out of their wayOwn the team's roadmap by understanding the technical work well enough to sequence it right and push back when priorities don't hold upRun recruiting as a technical process — define what good looks like for this stack, write the bar-raising questions, and close candidates because you can speak credibly to the workHandle the coordination and stakeholder management so the team doesn't have to — you're the buffer that lets engineers stay deepWhat You'll Bring12+ years of software engineering experience in data infrastructure, distributed systems, or backend platform engineeringHands-on production experience with Spark — you can tune jobs, understand the Catalyst optimizer, and have strong opinions on PySpark vs. Scala SparkFamiliarity with the modern lakehouse stack: Iceberg, Kafka/MSK, Airflow/MWAA, Trino or Athena, and cloud-native AWS servicesExperience with Snowflake, dbt, and building data access APIs or platform tooling2+ years of engineering management or formal tech lead experience — you've done performance reviews, owned a hiring loop, or led a team through a significant technical programComfort holding both hats: you can spend a morning in code review and an afternoon in a difficult growth conversation, and you're good at bothStrong communicator who can make technical trade-offs legible to product and business stakeholdersExperience in a startup or high-growth SaaS environmentExposure to AI/ML data pipelines or real-time analytics is a plus