Technical Leadership & Architecture: Drive data infrastructure strategy and establish standardized patterns for AI/ML workloads, with direct influence on architectural decisions across data and engineering teams
DataOps Excellence: Create seamless developer experience through self-service capabilities while significantly improving data engineer productivity and pipeline reliability metrics
Cross-Functional Innovation: Lead collaboration between DevOps, Data Engineering, and ML Operations teams to unify our approach to infrastructure as code and orchestration platforms
Technology Breadth & Growth: Work across the full DataOps spectrum from pipeline orchestration to AI/ML infrastructure, with clear advancement opportunities as a senior infrastructure engineer
Strategic Business Impact: Build scalable analytics capabilities that provide direct line of sight between your infrastructure work and business outcomes through reliable, cutting-edge data solutions
What you'll be doing
Design Data-Native Cloud Solutions - Design and implement scalable data infrastructure across multiple environments using Kubernetes, orchestration platforms, and IaC to power our AI, ML, and analytics ecosystem
Define DataOps Technical Strategy - Shape the technical vision and roadmap for our data infrastructure capabilities, aligning DevOps, Data Engineering, and ML teams around common patterns and practices
Accelerate Data Engineer Experience - Spearhead improvements to data pipeline deployment, monitoring tools, and self-service capabilities that empower data teams to deliver insights faster with higher reliability
Engineer Robust Data Platforms - Build and optimize infrastructure that supports diverse data workloads from real-time streaming to batch processing, ensuring performance and cost-effectiveness for critical analytics systems
Drive DataOps Excellence - Collaborate with engineering leaders across data teams, champion modern infrastructure practices, and mentor team members to elevate how we build, deploy, and operate data systems at scale
Requirements: What we're looking for
3+ years of hands-on DevOps experience building, shipping, and operating production systems.
Coding proficiency in at least one language (e.g., Python or TypeScript); able to build production-grade automation and tools.
Cloud platforms: deep experience with AWS, GCP, or Azure (core services, networking, IAM).
Kubernetes: strong end-to-end understanding of Kubernetes as a system (routing/networking, scaling, security, observability, upgrades), with proven experience integrating data-centric components (e.g., Kafka, RDS, BigQuery, Aerospike).
Infrastructure as Code: design and implement infrastructure automation using tools such as Terraform, Pulumi, or CloudFormation (modular code, reusable patterns, pipeline integration).
GitOps & CI/CD: practical experience implementing pipelines and advanced delivery using tools such as Argo CD / Argo Rollouts, GitHub Actions, or similar.
Observability: metrics, logs, and traces; actionable alerting and SLOs using tools such as Prometheus, Grafana, ELK/EFK, OpenTelemetry, or similar.
You might also have
Data Pipeline Orchestration - Demonstrated success building and optimizing data pipeline deployment using modern tools (Airflow, Prefect, Kubernetes operators) and implementing GitOps practices for data workloads
Data Engineer Experience Focus - Track record of creating and improving self-service platforms, deployment tools, and monitoring solutions that measurably enhance data engineering team productivity
Data Infrastructure Deep Knowledge - Extensive experience designing infrastructure for data-intensive workloads including streaming platforms (Kafka, Kinesis), data processing frameworks (Spark, Flink), storage solutions, and comprehensive observability systems
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