About the role

You will design and build scalable data infrastructure, including high-volume ingestion pipelines and foundational systems for analytics and AI. Additionally, you will manage Kubernetes-based infrastructure and provide technical guidance to the team to ensure best practices in system design.

What they look for

Data Platform Engineering Kubernetes Python SQL Terraform Data Pipelines Data Lake Distributed Systems Airflow Prefect Snowflake KEDA Infrastructure as Code CI/CD Memory Management Performance Tuning

Requirements

Candidates must have at least 5 years of experience in data platform engineering with a strong background in Data Lake architectures and Kubernetes. Proficiency in Python, SQL, and Infrastructure as Code tools like Terraform is required.

Full description

We are looking for a highly skilled Senior Data Platform Engineer to join our Data Platform team and play a key role in designing, building, and evolving the company’s data infrastructure.

This is a hands-on engineering position focused on developing scalable data platforms, high-volume ingestion pipelines, and the foundational data systems that power analytics, product applications, AI workloads, and large-scale model execution. You will help architect our data lake, build reliable ingestion capabilities at scale, and shape the core infrastructure that supports the company’s future growth.

This is an exciting opportunity to work with cutting-edge technologies and have a defining impact on the architecture and evolution of our data platform.

What You’ll Do

Build complex, high-volume batch and near-real-time ingestion services while helping evolve the platform’s streaming capabilities.
Design and implement data solutions for all application requirements in a distributed microservices environment
Develop and optimize large-scale data pipelines for batch and streaming use cases.
Ensure data quality, compliance, and governance.
Manage and optimize Kubernetes infrastructure, utilizing KEDA to dynamically scale resources for large-scale data processing workloads.
Own the full lifecycle of our data stack using Terraform and Kubernetes, from provisioning to application-level frameworks.
Provide technical guidance for the team, drive best practices around infrastructure, CI/CD, testing, and system design.
Deep-dive into memory management and performance tuning to ensure our large-scale distributed systems are both cost-effective and lightning-fast.

Requirements

5+ years of experience as a Data Platform/Infra - Proven track record of building and operating scalable data infrastructure specifically within Data Lake architectures.
Proven experience designing and operating large-scale data processing workloads on Kubernetes, including workload autoscaling with KEDA.
Deep experience building high-throughput Data Pipelines
Strong proficiency in Python and SQL.
Hands-on experience with Infrastructure as Code, preferably Terraform.
Experience with managing a data orchestration platform such as Airflow or Prefect
Experience with Snowflake.
You understand the "under the hood" mechanics of distributed computing, from memory allocation to data traceability.