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Senior Data Engineer - Data LeadGaruda Ventures • San Francisco, California, United States
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Senior Data Engineer - Data Lead

Garuda Ventures
  • US
    San Francisco, California, United States
  • US
    San Francisco, California, United States
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À propos

About Us Foundry Robotics is building an AI-native robotics manufacturing company focused on deploying advanced assembly and production capability for leading robotics companies and national-security-critical hardware. Basically, we’re building robots that build robots.
We are reimagining manufacturing through advanced robotics. Our mission is to rebuild the American manufacturing industry as an AI-first, assembly-focused, dual-use contract manufacturer. We aim to empower manufacturers with intelligent, efficient, and adaptable robotic systems that redefine productivity and quality.
The Role We are hiring a Sr Data Engineer / Data Lead to own the data layer that powers Factory OS. You will design and operate petabyte-scale data pipelines that ingest visual, telemetry, and manufacturing data from the factory floor, move it reliably across edge, on-prem, and cloud environments, and make it available for ML training, analytics, and operational decision-making. You will define the data roadmap—architecture, governance, lifecycle, and tooling—and be the person the rest of the engineering org depends on for clean, reliable, well‑modeled data. You will also contribute to backend services where data meets application logic. This is a hands‑on engineering role. You will ship.
Key Responsibilities Petabyte-Scale Data Pipelines
Design, build, and operate PB‑scale data pipelines for ingesting, curating, indexing, and preparing manufacturing, visual, and telemetry data
Implement reliable data movement across embedded, edge, on‑prem, and cloud compute environments
Build streaming and batch processing systems that handle high‑throughput factory‑floor data in real time
Implement data lifecycle management: retention, archival, compaction, and cost optimization at scale
Data Architecture + Roadmap
Define and own the Factory OS data roadmap—architecture, governance, quality, and tooling strategy
Design data models, schemas, and contracts that serve application, ML, and analytics consumers
Establish data cataloging, lineage tracking, and discoverability across the platform
Drive data quality standards: validation, monitoring, alerting, and anomaly detection
Evaluate and adopt data technologies (warehouses, lakehouses, streaming platforms) as the platform scales
Backend Services + Integration
Contribute to backend services where data pipelines meet application logic (APIs, event‑driven systems, database layers)
Partner with ML engineers to ensure training and inference pipelines have access to clean, well‑prepared datasets
Collaborate with full‑stack developers, robotics teams, and operations to translate data needs into reliable infrastructure
Implement observability: metrics, logging, and tracing across data systems
What We're Looking For
Between 5‑7 years of experience in data engineering, with demonstrated work at terabyte‑to‑petabyte scale
Deep hands‑on experience with data pipeline frameworks (Spark, Flink, Kafka, Airflow, dbt, or similar)
Strong proficiency in Python and SQL; backend experience in Go, Java, or TypeScript is a plus
Experience designing data architectures spanning streaming, batch, lakehouse, and warehouse patterns
Solid understanding of distributed systems, storage engines, and data modeling (relational and NoSQL)
Experience with cloud data services (AWS S3/Glue/Redshift, GCP BigQuery/Dataflow, or Azure equivalents)
Comfortable defining roadmaps, making architectural decisions, and driving alignment across teams
Comfortable operating independently and owning complex systems end‑to‑end
Nice to Have (Not Required)
Experience with visual or image data pipelines at scale (video ingestion, frame extraction, annotation workflows)
Background in manufacturing, robotics, or industrial IoT data systems
Familiarity with ML data preparation workflows (feature stores, dataset versioning, labeling pipelines)
Experience with edge‑to‑cloud data architectures and intermittent connectivity patterns
Exposure to infrastructure‑as‑code (Terraform, Pulumi) and container orchestration (Kubernetes)
Growth Opportunities
Define the entire data architecture and roadmap for an AI‑native factory
Work with petabyte‑scale visual, telemetry, and manufacturing data in a real production environment
Grow into a data leadership role as the team and platform scale
Direct influence on how data drives quality, throughput, and cost improvements in physical manufacturing
Why Join Us? This is one of the only places where world‑class manufacturing operators, mechanical engineers, robotics researchers, and software engineers sit in the same room — building production systems together.
We are committed to being deeply embedded in the U.S. industrial base. Our focus is simple: build adaptive robotic assembly systems that make American manufacturing scalable, resilient, and competitive again.
If you want to run a mature, well‑defined commercial org, this may not be the role.
If you want to build the commercial engine that brings AI‑driven manufacturing to every industrial and energy customer in America — this is it.
The base salary range for this full‑time position in the location of San Francisco is:
$150,000—$250,000 USD
Compensation packages at Foundry Robotics for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job‑related skills, experience, interview performance, and relevant education or training. Foundry Robotics employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. You'll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, and generous PTO.
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  • San Francisco, California, United States

Compétences linguistiques

  • English
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