About This RoleWe 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.
Responsibilities- 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.
Basic Qualifications- 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 Skills and Experience- 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.
Compensation and BenefitsThe base salary range for this role is
$125,000-$220,000 annually.
Compensation bands are determined by role, level, location, and alignment with market data. Individual level and base pay is determined on a case-by-case basis and may vary based on job-related skills, education, experience, technical capabilities and internal equity. Please note that the stated salary range is an estimate and may be adjusted based on market conditions, business needs, or other factors. In addition to base salary, for full-time hires, you may also be eligible for long-term incentives, in the form of stock options, and access to medical, vision & dental coverage as well as access to a 401(k) retirement plan.
