Senior Data Platform Engineer

Data Platform Engineer – Senior

Key Highlights

️ Lead the design and expansion of large-scale data platform components supporting analytics, experimentation, and machine learning workloads

⚡ Architect and optimize ETL, streaming, metadata, and federated querying systems at scale

Drive performance tuning, observability, and reliability efforts across distributed data environments

Own RBAC strategy, data governance patterns, and secure access controls for enterprise-wide users

Build and evangelize platform-level tooling that accelerates experimentation and raises analytical rigor

Position Overview

We are seeking a Senior Data Platform Engineer to architect, build, and scale a next-generation data platform. This is a high-impact role responsible for designing distributed systems, improving core platform capabilities, and guiding engineering teams in building reliable and performant data workflows.

You will lead initiatives across storage, streaming, querying, metadata, and experimentation, while influencing long-term platform direction and engineering standards.

Key Responsibilities

Data Platform Architecture & Engineering

Architect highly scalable data storage, processing, and transformation systems

Lead development of Iceberg-based lakehouse components and metadata systems

Design and optimize distributed ETL and streaming pipelines using Spark and Kafka

Build backend services in Python, Go, Scala, or Java to support platform capabilities

Improve Trino performance, query federation, and metadata scalability

Experimentation Platform Leadership

Develop and champion tools, frameworks, and processes that drive experiment validity

Partner with executives, product, and data science on experiment design and analytics strategy

Build automation that increases rigor and efficiency across experimentation workflows

Data Governance, Security & Reliability

Own RBAC strategy, IAM standards, and enterprise data access patterns

Lead observability initiatives using Datadog for logs, alerts, and distributed performance metrics

Ensure reliability, reproducibility, and schema governance across all data assets

Guide best practices for metadata integrity and data lifecycle management

Infrastructure & Deployment

Lead Kubernetes and Helm deployment patterns for data platform services

Build automation that improves operational efficiency and reduces platform friction

Drive capacity planning, scaling strategies, and performance tuning for large data systems

Technical Leadership & Mentorship

Influence platform roadmap and long-term architectural decisions

Mentor engineers across the team and support onboarding to platform systems

Write clear documentation and contribute to engineering playbooks

Qualifications

Degree in Computer Science, Engineering, or related field

5+ years in data platform, distributed systems, or backend platform engineering

Advanced proficiency in Python , Go , Scala , or Java

Deep experience with Iceberg-based data warehouses and lakehouse architectures

Expert-level knowledge of Spark , Kafka , Trino , and distributed querying systems

Strong experience with RBAC design, IAM security, and data governance tooling

Kubernetes, Helm, and CI / CD experience supporting production environments

Strong architectural thinking, systems design, and cross-team leadership skills

Why Join Us

This is an opportunity to shape the future of a large-scale data platform used across the organization. You’ll influence architecture, lead high-impact initiatives, and help build systems that power experimentation, analytics, and innovation at scale.