Data Platform Engineer

BrightAI; Palo Alto (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

BrightAI is seeking a Platform Data Engineer to design and maintain scalable data ingestion and processing pipelines. You will work across sensor and operational data streams from drones, robots, and industrial systems to support ML experiments.

You will build secure data lake architectures, deploy infrastructure with Terraform, and collaborate with Cloud, Edge, Product, and AI teams to ensure data contracts and retention policies.

Qualifications

• 5+ years of professional experience in software or data engineering.
• Strong programming skills in Python and Golang.
• Deep understanding of SQL and modern data lake architectures.
• Hands-on AWS experience: S3, Lambda, Glue, Kinesis, Athena, RDS.
• Proficiency with Terraform for automating infrastructure deployment.
• Hands-on experience with Databricks for ML workflows and Delta Lake.

Responsibilities

• Design, build, and maintain scalable, reliable data ingestion pipelines.
• Implement secure data lake and SQL-based storage architectures.
• Develop and maintain data ingestion tools using Python, Golang, and SQL.
• Build and maintain inference pipelines for model serving and near real-time predictions.
• Collaborate with Cloud, Edge, Product, and AI teams on data contracts and retention.
• Utilize AWS services to support end-to-end data workflows.
• Apply IaC practices with Terraform to manage platform infrastructure.
• Monitor pipelines for quality, latency, and failures with CloudWatch/DataDog.
• Land curated sensor data into Delta tables on Databricks for ML experiments.
• Continuously optimize storage and query performance across large datasets.

Skills

Python

Golang

SQL

Databricks

Kafka

Kinesis

Terraform

MLflow

Delta Lake

Spark SQL

Parquet

Iceberg

Tools

AWS

S3

Lambda

Glue

Kinesis

Athena

RDS

Terraform

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