<p>We are a high-growth company transforming how businesses operate by integrating AI, IoT, and cloud-native services into scalable, real-time platforms. As a Platform Data Engineer, you’ll play a critical role in building and maintaining the data infrastructure that powers our products, services, and insights. <br><br>You’ll join a multidisciplinary team focused on ingesting, processing, and managing massive streams of sensor and operational data across a wide array of devices—from drones and robots to industrial systems and smart environments. <br><br><strong>Responsibilities</strong></p> <ul> <li>Design, build, and maintain scalable, reliable, and high-throughput data ingestion pipelines for structured and semi-structured data.</li> <li>Implement robust and secure data lake and SQL-based storage architectures optimized for performance and cost.</li> <li>Develop and maintain internal tools and frameworks for data ingestion using Python, Golang, and SQL.</li> <li>Build and maintain inference pipelines to support model serving and near real time prediction workflows</li> <li>Develop and maintain internal tools and deployment processes to support ML development workflow , including experimentation, testing, deployment and monitoring.</li> <li>Collaborate cross-functionally with Cloud, Edge, Product, and AI teams to define data contracts, schemas, and retention policies.</li> <li>Use AWS cloud infrastructure (including Argo Workflows, S3, Lambda, Glue, Kinesis, Athena, and RDS) to support end-to-end data workflows.</li> <li>Employ Infrastructure-as-Code (IaC) practices using Terraform to manage data platform infrastructure.</li> <li>Monitor data pipelines for quality, latency, and failures using tools such as CloudWatch, SumoLogic, or DataDog.</li> <li>Build and maintain ingestion pipelines that land curated sensor and operational data into Delta tables on Databricks to support ML experimentation and model training.</li> <li>Continuously optimize storage, partitioning, and query performance across large-scale datasets.</li> <li>Participate in architecture reviews and ensure the platform adheres to security, compliance, and best practice standards.</li> </ul> <p><strong>Skills and Qualifications</strong></p> <ul> <li>5+ years of professional experience in software engineering or data engineering.</li> <li>Strong programming skills in Python and Golang.</li> <li>Deep understanding of SQL and modern data lake architectures (e.g., using Parquet, Iceberg, or Delta Lake).</li> <li>Hands-on experience with AWS services including but not limited to: S3, Lambda, Glue, Kinesis, Athena, and RDS.</li> <li>Proficiency with Terraform for automating infrastructure deployment and management.</li> <li>Hands-on experience with Databricks for ML workflows: MLflow experiment tracking and model registry, Model Serving, and building training datasets in Delta Lake (PySpark/Spark SQL).</li> <li>Experience working with real-time or batch data ingestion at scale, and designing fault-tolerant ETL/ELT pipelines.</li> <li>Familiarity with DVC for dataset versioning and management.</li> <li>Familiarity with event-driven architectures and messaging systems like Kafka or Kinesis.</li> <li>Strong debugging and optimization skills across cloud, network, and application layers.</li> <li>Excellent collaboration, communication, and documentation skills.</li> </ul> <p><strong>Bonus Points</strong></p> <ul> <li>Experience working with time-series or IoT sensor data at industrial scale.</li> <li>Exposure to gRPC and protobuf-based data contracts.</li> <li>Experience supporting ML pipelines and feature stores.</li> <li>Working knowledge of Kubernetes concepts.</li> <li>Databricks Machine Learning certification (Professional)</li> <li>Prior startup experience and/or comfort working in fast-paced, iterative environments.</li> </ul>