Member of Technical Staff - Engineering Lead, Data Platform
Posted 30 Sep 2026
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- People management
- Employment
- On-Site
- Contract
- Full-time
- Visa sponsorship
- Offered
No salary listed for this role.
Our Mission
Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all.
About the role
Reflection is building the trusted data backbone for the world's most capable open-weight AI systems. The Data Platform team builds and operates the core data systems and pipelines that power our research, training, and production environments unifying ingestion, processing, and orchestration across the entire data lifecycle so every team can move faster.
We're looking for a front-line technical leader to build, mentor, and grow this team. You'll guide its technical direction while staying hands-on this is a role for someone who has earned deep technical credibility as an engineer and now multiplies it through a team. You'll work closely with our research team to understand what high-velocity experimentation actually needs, and turn that into reliable, reproducible, scalable data infrastructure.
What you'll do
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Build, mentor, and grow a team of ~10 exceptional data platform engineers hiring, coaching, and raising the bar with every person you add.
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Guide the technical direction across the platform: ingestion and orchestration patterns for batch and streaming workloads, scalable compute and storage foundations, and reproducible pipelines with versioning, backfills, and isolated execution environments.
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Work closely with research, training, and production teams to enable high-velocity experimentation on a unified data layer.
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Establish trusted data quality, lineage, and governance signals so teams can make confident production decisions.
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Keep cost and performance predictable through guardrails, budgets, and continuous system tuning.
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Stay hands-on: design reviews, architecture decisions, and code where it matters most.
What you'll work with
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Compute & orchestration: Spark, Flink, Beam, Airflow, Dagster, Kafka, PubSub
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Storage & analytics: data lake and warehouse architectures, Parquet, Iceberg, Delta Lake, BigQuery, Snowflake
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Metadata & data quality: lineage systems, metadata management, Great Expectations, reproducibility systems