Engineering Manager, Data Platform

As 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 Do

As a technical contributor (≈50%)

Write production code and lead architectural design on the data platform — pipelines, storage layers, access APIs, and observability infrastructure

Own technical decisions on Spark job structure, Iceberg table management, and streaming architecture on MSK/Kafka

Review code at a level that raises the bar, not just catches bugs — you shape how the team thinks about distributed systems

Drive modernization and tooling choices (Snowflake, dbt, Airflow, Trino, and what comes next) with a bias toward operational simplicity and long-term maintainability

Contribute 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 side

Give engineers real feedback in the moment: on a PR, in a design review, after a demo — not just in quarterly check-ins

Spot 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 way

Own the team's roadmap by understanding the technical work well enough to sequence it right and push back when priorities don't hold up

Run 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 work

Handle the coordination and stakeholder management so the team doesn't have to — you're the buffer that lets engineers stay deep

What You'll Bring

12+ years of software engineering experience in data infrastructure, distributed systems, or backend platform engineering

Hands-on production experience with Spark — you can tune jobs, understand the Catalyst optimizer, and have strong opinions on PySpark vs. Scala Spark

Familiarity with the modern lakehouse stack: Iceberg, Kafka/MSK, Airflow/MWAA, Trino or Athena, and cloud-native AWS services

Experience with Snowflake, dbt, and building data access APIs or platform tooling

2+ 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 program

Comfort holding both hats: you can spend a morning in code review and an afternoon in a difficult growth conversation, and you're good at both

Strong communicator who can make technical trade-offs legible to product and business stakeholders

Experience in a startup or high-growth SaaS environment

Exposure to AI/ML data pipelines or real-time analytics is a plus