Job description

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.

Requirements

Must-haves

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.

Nice-to-haves

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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aisqlpythongolangmlawskubernetesmachine learning

Platform: greenhouse
Effort: quick