Data Platform Engineer

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.

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.

Responsibilities

• Design, build, and maintain scalable, reliable, and high-throughput data ingestion pipelines for structured and semi-structured data.

• Implement robust and secure data lake and SQL-based storage architectures optimized for performance and cost.

• Develop and maintain internal tools and frameworks for data ingestion using Python, Golang, and SQL.

• Build and maintain inference pipelines to support model serving and near real time prediction workflows

• Develop and maintain internal tools and deployment processes to support ML development workflow , including experimentation, testing, deployment and monitoring.

• Collaborate cross-functionally with Cloud, Edge, Product, and AI teams to define data contracts, schemas, and retention policies.

• Use AWS cloud infrastructure (including Argo Workflows, S3, Lambda, Glue, Kinesis, Athena, and RDS) to support end-to-end data workflows.

• Employ Infrastructure-as-Code (IaC) practices using Terraform to manage data platform infrastructure.

• Monitor data pipelines for quality, latency, and failures using tools such as CloudWatch, SumoLogic, or DataDog.

• Build and maintain ingestion pipelines that land curated sensor and operational data into Delta tables on Databricks to support ML experimentation and model training.

• Continuously optimize storage, partitioning, and query performance across large-scale datasets.

• Participate in architecture reviews and ensure the platform adheres to security, compliance, and best practice standards.

Skills and Qualifications

• 5+ years of professional experience in software engineering or data engineering.

• Strong programming skills in Python and Golang.

• Deep understanding of SQL and modern data lake architectures (e.g., using Parquet, Iceberg, or Delta Lake).

• Hands-on experience with AWS services including but not limited to: S3, Lambda, Glue, Kinesis, Athena, and RDS.

• Proficiency with Terraform for automating infrastructure deployment and management.

• 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).

• Experience working with real-time or batch data ingestion at scale, and designing fault-tolerant ETL/ELT pipelines.

• Familiarity with DVC for dataset versioning and management.

• Familiarity with event-driven architectures and messaging systems like Kafka or Kinesis.

• Strong debugging and optimization skills across cloud, network, and application layers.

• Excellent collaboration, communication, and documentation skills.

Bonus Points

• Experience working with time-series or IoT sensor data at industrial scale.

• Exposure to gRPC and protobuf-based data contracts.

• Experience supporting ML pipelines and feature stores.

• Working knowledge of Kubernetes concepts.

• Databricks Machine Learning certification (Professional)

• Prior startup experience and/or comfort working in fast-paced, iterative environments.