Join a small engineering team at a growth-stage logistics software company focused on shipment visibility and risk management for temperature-sensitive goods. You will own the integrations that turn data from devices, carriers, and customer systems into reliable information teams can act on.
Build and operate ingestion pipelines for device telemetry, carrier milestones, and customer order data using APIs, webhooks, polling, files, and queues.
Map and normalize source data into shared shipment and telemetry models, with validation and quality checks throughout the pipeline.
Monitor pipeline health, build alerts and dashboards, and handle replays or backfills when data is late or incorrect.
Use partner API specifications and sample data to deliver new integrations.
Turn one-off integrations into reusable, configuration-driven pipelines.
At least 5 years of experience building production data pipelines or backend data systems, with ownership of external integrations from ingestion through monitoring and support.
Strong Python skills and familiarity with TypeScript or Node.js, or the ability to ramp up quickly.
Proficiency with SQL, data modeling, batch and incremental ETL/ELT, schema mapping, and data quality practices.
Experience integrating external APIs, including rate limits, retries, and pagination, as well as operating cloud services on Azure or AWS.
Hands-on experience with testing, CI/CD, monitoring, and observability for production systems.
Experience with IoT, telemetry, logistics, regulated data, event-driven systems, or data processing tools such as Spark is a plus.
Base salary is $140,000 to $180,000 annually, with equity and a cash bonus. Visa sponsorship is available.
Remote within the United States.
Build and optimize Databricks data platforms and pipelines across batch and streaming workloads, contribute technical leadership and solution accelerators, and help deliver scalable client data solutions.
Lead delivery of Databricks data engineering solutions, designing and optimizing scalable batch and streaming pipelines while contributing to data platform architecture and reusable solution accelerators.
Lead Databricks data engineering work to design and implement scalable lakehouse platforms and batch and streaming pipelines, while contributing to solution architecture, data quality, and Databricks accelerators.
Own and modernize scalable data infrastructure for a cybersecurity product by improving pipelines, data models, and SQL performance while driving reliability and architectural improvements.
Design and operate scalable, secure data pipelines and APIs for data-driven and AI-enabled products, while shaping engineering standards, leading technical reviews, and mentoring engineers.
