Senior Engineering Manager, Data Platform

2 нед. назад

249k–299k USD / yearUSASeniorOnsite

data platformengineering managementairoboticssoftware development

Lead engineering efforts for the data platform at , an autonomous manufacturing factory company.

Обязанности

At , data is part of the factory's operating system. The scheduler deciding what runs next, models predicting cycle time and demand, systems interpreting engineering drawings, analytics tracking production, and operators understanding what is happening on the floor all depend on the same thing: a trustworthy path from the physical and software systems that generate data to the teams and products that use it.
The Data Platform team owns that path. We build the shared infrastructure for ingestion and CDC, event streaming, the warehouse and lakehouse, orchestration, data contracts, quality, lineage, and the foundational data models that sit underneath Analytics, Data Science, Operations Research, ML, and operational software. Today that includes Snowflake, Iceberg on S3, PeerDB, Redpanda/Kafka, Dagster, dbt, Kubernetes, and protobuf/gRPC. Some of our data begins in conventional software systems. Some begins on CNC machines, inspection equipment, PLCs, engineering files, and the factory floor.
As an Senior Engineering Manager, you will build and lead the team responsible for making this platform reliable, coherent, and useful as scales to many factories. You will own the people, execution, operating quality, and direction of the team while staying technically close enough to challenge architecture, understand failure modes, and help engineers make good tradeoffs.

Требования

To conform to U.S. Government space technology export regulations, including the International Traffic in Arms Regulations (ITAR) you must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here .

Условия

For this role, the target salary range is $249,000 - $299,000 (actual range may vary based on experience).
This is the lowest to highest salary we reasonably and in good faith believe we would pay for this role at the time of this posting. We may ultimately pay more or less than the posted range, and the range may be modified in the future. An employee's pay position within the salary range will be based on several factors, including, but not limited to, relevant education, qualifications, certifications, experience, skills, geographic location, performance, and business or organizational needs.
Medical, dental, vision, and life insurance plans for employees
401k
Relocation support may be provided for certain situations, based on business need.
Flexible vacation policy
Equity

Другое

is building autonomous factories to reindustrialize America. By combining AI, advanced software, robotics, and full-stack manufacturing, we help aerospace and defense companies build rockets, satellites, aircraft, ships, and other mission-critical systems up to 10x faster and at significantly lower cost.
Following our $1.37B Series D at a $7.87B valuation, is rapidly expanding our manufacturing footprint, launching new capabilities across welding, casting, forging, electronics, additive manufacturing, and more, while scaling our Factory-as-a-Service platform to transform how critical products are built.
Backed by leading investors including JPMorgan Chase, Valor Equity Partners, Andreessen Horowitz, Founders Fund, 137 Ventures, Lux Capital, T. Rowe Price, and Morgan Stanley, we’re building the future of American manufacturing—and looking for exceptional people to help make it happen.
If you’re ready to take on the most challenging and rewarding work of your career while helping create American manufacturing jobs for generations to come, you’re exactly who we’re looking for.
Build the team and platform that become 's data backbone. Lead the engineers responsible for the path from machines and operational systems to the trusted datasets, streams, and interfaces that scheduling, analytics, ML, software, and factory operations depend on.
Own the Data Platform roadmap and delivery. Turn ambiguous needs across Software, Infrastructure, Analytics, Data Science, Operations Research, ML, and Manufacturing into clear priorities, interfaces, owners, and commitments.
Raise the operational bar for the platform: reliability, data-quality SLAs, observability, incident response, backfills and recovery, cost, security, schema evolution, provenance, and clear ownership when something fails.
Partner with senior engineers and technical leads on architecture across CDC and streaming, Snowflake and Iceberg, orchestration, data contracts, schema governance, and the interfaces between the data platform and its consumers. You do not need to design every system yourself, but you need the depth to ask the hard questions and recognize when important semantics or failure modes are missing.
Turn repeated one-off work into paved roads. Build standards, contracts, tooling, documentation, and migration paths that let other engineering and data teams move faster without creating a collection of bespoke pipelines.
Hire and grow an exceptional team. Set a clear technical and performance bar, give direct feedback, develop engineers into broader technical leaders, delegate real ownership, and build a team whose capability grows faster than its headcount.
A track record leading engineers responsible for production data infrastructure, distributed systems, developer platforms, infrastructure, or similarly critical technical systems. For Senior EM, you have led multiple substantial workstreams or teams and developed senior technical leaders.
Strong technical judgment in modern data and platform systems: event streaming and CDC, warehouses and lakehouses, orchestration, schema evolution, data modeling, distributed-system failure modes, and the tradeoffs behind reliable data delivery.
Experience building for environments with real constraints. You understand that the architecture changes when data comes from hardware, machines, devices, files, telemetry, regulated systems, edge environments, or other sources that do not behave like a clean application database.
A real platform mindset. You think in shared abstractions, contracts, ownership, operating guarantees, adoption, migration, deprecation, and paved roads — not just a set of infrastructure components owned by one team.
An operator's mindset. You know what your systems promise, how you know they are healthy, what happens when those promises fail, and how an incident becomes a permanent improvement rather than a recurring story.
Strong people leadership and high agency across organizational boundaries. Your engineers know what is expected of them, where they stand, and how they can grow; when a dependency is blocking the team, you go resolve it rather than simply reporting it upward.
5+ years of management experience in manufacturing, semiconductors, hardware, robotics, autonomy, industrial systems, test/measurement, or edge computing, especially where the data platform had to bridge software with physical systems. Backgrounds from companies such as NVIDIA, AMD, Intel, Xilinx, Altera, Qualcomm, Applied Materials, Lam Research, Tesla, Anduril, Shield AI, or similar environments are especially relevant.
Experience with heterogeneous data beyond application tables: telemetry, events, files, device identity, timestamps and clock drift, units, provenance, schema drift, deletes, late or out-of-order data, partial records, retries, and backfills.
Experience taking a data or infrastructure platform from an early working system to a broadly adopted internal product used by many teams.
Experience operating in security-sensitive or regulated environments such as aerospace, defense, FedRAMP, CMMC, or other environments where data governance and access control are part of the architecture.
A track record of building strong hiring and onboarding systems and helping senior engineers become effective technical leads, Staff+ engineers, or future managers.
uses AI-assisted tools in our recruiting and hiring processes to help our team work more efficiently. This may include tools that help organize and analyze recruiting data, as well as an AI-powered notetaker that can record and transcribe interviews and help coordinate feedback. These tools support our team and are not used to make hiring decisions. All candidate evaluations and hiring decisions are performed by humans. If an interview will be recorded, you will be notified in advance and may opt out at any time with no impact on your candidacy. Candidate data processed through these tools is subject to the same protections described in our Privacy Policy .
It is the Company’s policy to provide equal employment opportunity for all applicants and employees. The Company does not unlawfully discriminate on the basis of race inclusive of traits historically associated with race (including, but not limited to, hair texture and protective hairstyles, such as braids, locks and twists), color, religion, sex (including pregnancy, childbirth, or related medical conditions), gender identity, gender expression, transgender status, national origin (including, in California, possession of a drivers license), ancestry, citizenship, age, physical or mental disability, height or weight, medical condition, family care status, military or veteran status, marital status, domestic partner status, sexual orientation, genetic information, exercise of reproductive r