Software Engineer, Data Platform

Los Angeles, CAfull timeOn Site

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mid

$125k–$220k

H1B: no

Generalmatter Software Engineer, Data Platform at Generalmatter designs and implements data acquisition, storage, and streaming systems for test stands and production. The role builds scalable data pipelines for high-throughput sensor telemetry to support visualization and analytics.

Responsibilities

We are seeking a Data Platform Software Engineer to design and implement the data acquisition, storage, and streaming backbone for test stands and production systems. This role sits at the intersection of industrial data systems and high-performance backend engineering, and is responsible for building reliable, scalable data pipelines that ingest high-throughput, high-channel-count sensor telemetry without loss and serve it to visualization and analytics layers. The successful candidate will play a central role in defining the architecture, schema design, and operational reliability of mission-critical data infrastructure.
Design, implement, and maintain high-throughput data pipelines for kHz-class, high-channel-count sensor telemetry across test and production environments.
Design and operate time-series storage and industrial data historians for long-term retention, query performance, and schema evolution.
Build ingestion and streaming services with a focus on buffering, backpressure, idempotency, and no silent data loss.
Define and champion modern software engineering practices, data architecture, schema standards, testing strategies, and operational best practices across the data platform.
Work closely with controls, test automation, and visualization teams to feed downstream analytics and live dashboards directly.
Lead debugging, root cause analysis, and reliability improvements across the full ingestion-to-storage stack.

Qualification

StrongExperience with high-reliabilityBackground in observabilityAdditional Requirements

Required

Bachelor’s degree or higher in Computer Science, Computer Engineering, Software Engineering, or a related field.
5+ years of experience building data pipelines, streaming systems, or backend data infrastructure.
Strong, production-level experience in Python and SQL.
Hands-on experience with high-throughput data ingestion and time-series or historian storage, with a strong understanding of distributed systems and data reliability.
Experience with high-reliability, on-premise, 24/7 deployments in safety-critical environments.

Preferred

Experience with time-series databases (e.g., InfluxDB, TimescaleDB), in-memory data stores (e.g., Redis), and OT/industrial data historians.
Experience with streaming and pub/sub middleware (e.g., MQTT, DDS, OPC-UA, Kafka).
Experience with high-channel-count sensor data and reliability engineering for ingestion.
Background in observability, infrastructure-as-code, and feeding visualization layers (e.g., Grafana).
Background in high-reliability or data-intensive environments (e.g., industrial automation, aerospace, energy, finance/trading, telecom, scientific research).
Demonstrated technical leadership in setting engineering standards, and building and mentoring high-performance engineering teams.
Additional Requirements
Ability to work extended hours and weekends as necessary.