Senior DevOps Engineer

We bring startups and disruptive tech companies to the local market, offering engineers the opportunity to work on innovative, high-impact products. Join us and become a member of their R&D team while we take care of employment, HR support, a benefits package, and an enjoyable work environment.

Unity Aura is a large-scale mobile platform focused on content discovery at scale. The product serves millions of users and is built in a fast-moving, high-growth environment.

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The stack reflects that: modern technologies, with a strong emphasis on scalability and resilience as the platform continues to expand.

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This role suits someone who wants to work on a technically complex product with real reach and see the impact of their decisions in production.

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A good fit here is a team-oriented engineer with strong technical judgment, a sense of ownership, and the ability to choose the right tools for the problem at hand.

You’ll take a leading role in shaping our data infrastructure, setting clear architectural standards for AI/ML workloads and influencing decisions across data and engineering teams.

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The focus is on building a smooth, self-service developer experience while improving pipeline reliability and day-to-day productivity for data engineers.

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You’ll work closely with DevOps, Data Engineering, and ML Ops to align on infrastructure-as-code and orchestration practices, covering the full DataOps stack from pipelines to AI/ML infrastructure.

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• 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
• 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
• Collaborate on high-level technical designs with ML and Backend engineers to build resilient systems

• 5+ years of hands-on DevOps experience building, shipping, and operating production systems
• Infrastructure as Code: design and implement infrastructure automation using tools such as Terraform, Pulumi, or CloudFormation (modular code, reusable patterns, pipeline integration)
• 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).
• 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
• Scalability & Performance: Proven experience managing production environments characterized by high traffic volumes and large amounts of data, with a focus on maintaining system reliability and cost-efficiency at scale

• Coding proficiency in at least one language (e.g., Python or TypeScript); able to build production-grade automation and tools
• Data Pipeline Orchestration: Demonstrated success building and optimizing data pipeline deployment using modern tools (Airflow, Temporal, 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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