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NEXTMOVEFDE careers · United States

Senior Director, Data Platform Engineering

Technologies

Kafka · Flink · DuckDB · ClickHouse · AWS · GCP · S3 · Athena · BigQuery · Kubernetes · Python

About Lila Sciences

Autonomous AI platform that executes the scientific method end-to-end—from hypothesis generation to experimental execution—for biotech, materials, and chemical R&D.

Series A

Job description

The full responsibilities and requirements are on the employer’s site.

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Source and classification

Deployment team leadership · Evidence for this classification:

Your Impact at LILA Lila is seeking a highly motivated and experienced engineering leader to lead a team responsible for our Lila's product data platform. You will own the data platform and infrastructure end-to-end — architecture, delivery, reliability, and developer/data scientist experience. Our mission is to deliver Scientific Super Intelligence through a reliable, scalable, and self-service infrastructure for data ingestion, storage, processing, and interaction — enabling AI/ML, product teams, and scientists to build data-intensive applications with confidence and speed. Our platform supports analytical and machine learning workloads across Lila, serving autonomous DBTL cycles, instrument data pipelines, and AI inference workflows. You will be responsible for building and leading a team of talented engineers, driving technical strategy, and ensuring the scalability and
More from the job description

Your Impact at LILA Lila is seeking a highly motivated and experienced engineering leader to lead a team responsible for our Lila's product data platform. You will own the data platform and infrastructure end-to-end — architecture, delivery, reliability, and developer/data scientist experience. Our mission is to deliver Scientific Super Intelligence through a reliable, scalable, and self-service infrastructure for data ingestion, storage, processing, and interaction — enabling AI/ML, product teams, and scientists to build data-intensive applications with confidence and speed. Our platform supports analytical and machine learning workloads across Lila, serving autonomous DBTL cycles, instrument data pipelines, and AI inference workflows. You will be responsible for building and leading a team of talented engineers, driving technical strategy, and ensuring the scalability and performance of our data management and data serving capabilities of our Data Platform. You will work closely with data scientists, data engineers, lab scientists, and product teams to understand their needs and deliver innovative solutions that leverage the power of cutting edge data processing technologies. What You'll Be Building Team Leadership: Build, mentor, and manage a high-performing team of 30-40 data engineering experts. Evaluate and adopt modern data infrastructure - including real-time strea [... source excerpt omitted ...] ices. Stakeholder Management: Partner with data scientists, data engineers, lab scientists, product managers, and other stakeholders to understand their data processing needs and requirements; Communicate technical concepts and solutions effectively to both technical and non-technical audiences; Advocate for best practices in data processing and engineering; Manage expectations and ensure alignment across different teams. Engineering Thought Leadership: Represent Lila’s data platform work at external conferences; Deliver presentations, and write blog posts highlighting Lila’s leadership in big data processing. Scientist and Engineering Productivity: Drive innovative, agentic, and [... source excerpt omitted ...] r engineers. Experience with building on AWS/GCP primitives like S3 + Athena/BigQuery, and query engines. Operated data platforms at petabyte scale with sub-second query latency requirements. Experience managing data infrastructure supporting 100+ concurrent ML training and inference workloads. Familiarity with LLM/AI-native data patterns — vector stores, embedding pipelines, pre/mid/post training. Track record of building data platforms in high-growth or early-stage environments where speed-to-value mattered as much as long-term architecture. Hands-on coding in Python and modern backend frameworks. Experience with infrastructure-as-code and containerized deployments (Kubernete

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