Senior Data DevOps Engineer

Appliscale is an independent Technology Consultancy specializing in bespoke, cloud-based systems and full-cycle engineering across industries such as gaming, proptech, legaltech, and adtech. They partner with startups, scaleups, and Fortune 500 companies worldwide and focus on building intelligent agents for automation and scalable agentic ecosystems.

• Minimum 5 years commercial experience in software engineering, data engineering, or platform engineering
• Bachelor's or higher degree in Computer Science, Software Engineering, or related field
• Commercial coding experience in Java, Python, or Golang
• Knowledge of Infrastructure as Code tooling such as Terraform
• Experience with CI/CD tools like GitHub Actions, Jenkins, and Docker
• Experience with streaming and schema-driven systems like Kafka, Protobuf, and JSON Schema
• Commercial experience with Airflow and DBT (Data Build Tool)
• Commercial experience with Databricks and Spark/PySpark

Nice to have:

• Experience with Databricks Delta Live Tables and Unity Catalog
• Experience with AWS components including Kafka/MSK, S3, IAM, and VPC/networking
• Experience with Buf, Protobuf tooling, or JSON-to-Protobuf migration
• Familiarity with Golang for tooling or automation
• Experience with infrastructure monitoring and on-call processes using Datadog and PagerDuty
• Ability to troubleshoot and optimize complex ETL pipelines

• Design, build, and operate services and pipelines for analytics data ingestion, schema management, and delivery into Databricks
• Modernize legacy platform components by migrating to modern Java and application standards
• Build automation, CI/CD flows, and integration test coverage
• Design and maintain production infrastructure in AWS using Infrastructure as Code tools such as Terraform
• Improve data quality with schema validation, metadata capture, lineage tracking, and operational guardrails
• Build tooling to assist internal customers migrating to new ingestion and schema standards
• Collaborate with cross-functional teams on design docs, rollout plans, and implementation strategies
• Monitor live systems, investigate incidents, and participate in on-call for third-line support
• Support performance, reliability, and cost optimization of ingestion and processing flows