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Harmony Data Integration Technologies is seeking a DevOps Lead to design, build, and operate scalable cloud and data platforms, primarily on GCP with AWS as secondary. This hybrid role covers platform engineering, Terraform, Kubernetes, CI/CD, security, observability, and data pipelines.
The ideal candidate combines data engineering experience with DevOps/Platform Engineering, wearing infrastructure and automation hats while managing production data workloads.
We are looking for a highly skilled and motivated DevOps Lead to design, build, and operate scalable, secure, and highly available cloud and data platforms, primarily on Google Cloud Platform (GCP) with Amazon Web Services (AWS) as a secondary environment.
This is a hybrid platform role. Alongside core DevOps ownership - Terraform, Kubernetes, CI/CD, cloud security, and observability - you will also contribute substantially to our data engineering function: building and operating production Python data pipelines, orchestrating batch and streaming workflows, and owning the infrastructure that sits underneath our data platform.
The ideal candidate has spent several years building data pipelines as a data engineer and has since moved into a DevOps / SRE / Platform Engineering role, or has run platform engineering for data-intensive products. We are looking for someone who can credibly wear both hats: infrastructure and automation first, data engineering a close second.
-8+ years of total professional experience in software, data, or infrastructure engineering.
-Minimum 4 years in a dedicated DevOps / SRE / Platform Engineering role.
-Substantial prior hands‑on data engineering experience at a senior level (pipeline development, data warehousing, and orchestration).
-Strong proficiency in Google Cloud Platform (GCP) and solid working experience with AWS.
-Hands‑on experience with Terraform and Infrastructure as Code (IaC).
-Strong experience with Kubernetes, Docker, and Helm in production.
-Expertise with CI/CD tools: Jenkins, GitLab CI, GitHub Actions, ArgoCD.
-Strong production‑grade Python - not scripting only, but maintainable pipeline and tooling code - plus solid Bash.
-Advanced SQL and hands‑on experience with a cloud data warehouse (BigQuery preferred; Snowflake or Redshift also considered).
-Experience with workflow orchestration (Airflow / Cloud Composer) and a distributed processing framework (Dataflow/Apache Beam, Spark, or dbt).
-Experience with streaming or event‑driven data (Pub/Sub, Kafka, or Kinesis).
-Experience with SonarQube and static code analysis integration.
-Practical cloud security experience (IAM, VPC, firewalls/security groups).
-Secrets management experience (Secret Manager / Secrets Manager / Vault).
-Experience with image and dependency scanning.
-Knowledge of secure software development practices.
-Excellent written and verbal communication skills.
-Certifications: GCP Professional DevOps Engineer, GCP Professional Data Engineer, GCP Architect, AWS DevOps Engineer, etc.
-Experience with dbt and modern analytics engineering practices.
-Data modelling experience (dimensional modelling, lakehouse formats such as Iceberg or Delta Lake).
-Data quality and testing frameworks (Great Expectations, Soda, dbt tests).
-Exposure to ML platform support (Vertex AI, MLflow, feature stores, model deployment pipelines).
-Knowledge of compliance frameworks (ISO 27001, CIS Benchmarks, NIST) and data governance / PII handling.
-Experience with monitoring tools (Prometheus, Grafana).
-Experience with logging stacks (ELK, Loki, etc.).
-Exposure to policy-as-code and security tools (OPA, scanners).
-Cloud cost optimisation / FinOps experience, particularly for data workloads.
-Scripting or service development in Go.