Data Platform Engineering Lead & Team Mentor

Reflection; San Francisco (CA); On-site; USD 230,000 - 320,000; Full time

Benefits offered by this job

Top-tier compensation

Stock options

Health & wellness

Meals provided in office

Parental leave (22 weeks)

Job summary

Reflection is seeking a frontline technical leader to build, mentor, and grow a data platform team responsible for ingestion, orchestration, compute, and data quality across the data lifecycle. You will partner with research and production to enable rapid experimentation on a unified data layer.

You will guide architecture while staying hands-on, ensuring scalable, cost-conscious data infrastructure and a high-trust engineering culture that accelerates AI research.

Qualifications

• 8+ years of engineering experience with a strong data engineering foundation.
• Track record as a staff/principal-level IC before moving into leadership.
• Managed and grown a team (~5–10) with technical embedding.
• Deep experience in ingestion/orchestration, storage/processing engines, or data quality/lineage.

Responsibilities

• Build, mentor, and grow a team of ~10 data platform engineers.
• Guide technical direction across ingestion, orchestration, and scalable compute/storage.
• Collaborate with research, training, and production teams for high-velocity experimentation.
• Establish data quality, lineage, and governance signals for production decisions.
• Maintain cost and performance through guardrails and tuning.
• Stay hands-on with design reviews and architecture decisions.

Skills

Data engineering

Leadership

People management

Cloud data platform

Tools

Spark

Flink

Airflow

BigQuery

Snowflake

Job description

Reflection is seeking a frontline technical leader to build, mentor, and grow a data platform team responsible for ingestion, orchestration, compute, and data quality across the data lifecycle. You will partner with research and production to enable rapid experimentation on a unified data layer.

You will guide architecture while staying hands-on, ensuring scalable, cost-conscious data infrastructure and a high-trust engineering culture that accelerates AI research.