5+ years of experience in software or data engineering.
Strong skills in Python and Golang programming.
Deep knowledge of SQL and modern data lake architectures.
Hands-on with AWS services like S3, Lambda, and Kinesis.
Proficient in Terraform for infrastructure management.
Experience in real-time or batch data ingestion and designing ETL pipelines.
Ability to debug and optimize across various technological layers.
Responsibilities
Design and maintain scalable data ingestion pipelines.
Implement secure data lake and SQL storage architectures.
Develop internal tools for data ingestion using Python and Golang.
Collaborate with cross-functional teams to define data contracts and schemas.
Utilize AWS infrastructure to support data workflows.
Employ Infrastructure-as-Code practices with Terraform.
Monitor data pipeline performance for quality and latency.
Benefits
High-growth company with a focus on innovative technologies.
Opportunity to work with a multidisciplinary team on cutting-edge projects.
Involvement in architecture reviews focusing on security and compliance standards.
Full Job Description
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.
