Cloud Engineer - GCP & Databricks

Lead technical workshops and requirements-gathering sessions with client technical and business teams. Produce high-quality client-facing deliverables: architecture diagrams, technical specifications, migration runbooks, and data dictionaries. Present solution designs and progress updates to client stakeholders — adapting technical communication to audience level (CTO to Business Analyst).

Manage technical dependencies, risks, and issues across delivery workstreams — escalating to Delivery Managers as appropriate. Participate in sprint ceremonies and maintain delivery velocity within Agile/Scrum or Kanban frameworks.

Implement data quality frameworks using tools such as Great Expectations, dbt tests, or Databricks Delta constraints. Support data catalogue initiatives and metadata management using Dataplex or Unity Catalog. Ensure GDPR/data residency compliance is embedded into solution design from the outset.

Support practice leads in responding to RFPs and RFIs — contributing to effort estimation, solution sizing, and proposal narrative. Contribute to internal knowledge-sharing — through documentation, runbooks, and team enablement sessions. Stay current with GCP and Databricks product roadmaps and proactively identify opportunities to introduce new capabilities to clients.

Must have qualifications:

* Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent practical experience.

* 6+ years of hands-on professional experience in cloud data engineering or GCP platform roles.

* Google Cloud Professional Data Engineer or Professional Cloud Architect certification.

* Databricks Certified Associate Developer for Apache Spark or Databricks Certified Data Engineer certification.

* BigQuery — advanced SQL, partitioning, clustering, cost optimization.

* Cloud Storage, Cloud Functions, Cloud Run.

* Dataflow (Apache Beam) — batch & streaming pipelines.

* Cloud Composer / Airflow — DAG authoring and management.

* Pub/Sub — event-driven architectures.

* Vertex AI — model serving and Pipelines exposure.

* IAM, VPC, Org Policies — security and governance.

* Cloud Build / Artifact Registry — CI/CD and containerization.

* Terraform — infrastructure as code on GCP.

* Looker / Looker Studio — reporting layer familiarity.

* PySpark — DataFrame API, optimization, broadcast joins.

* Delta Lake — ACID transactions, time travel, Z-ordering.

* Unity Catalog — governance, lineage, access control.

* Databricks Workflows / Job orchestration.

* Databricks on GCP — cluster config, instance pools.

* MLflow — experiment tracking, model registry.

* dbt on Databricks or BigQuery.

* Databricks Asset Bundles / CI/CD integration.

* Python — data engineering and scripting proficiency.

* SQL — advanced analytical queries.

* Git / version control — branching, PR workflows.

* Docker — containerization basics.

* Unit testing and data pipeline testing practices.

* REST API integration patterns.

Nice to have qualifications:

* Experience with Azure or AWS alongside GCP — hybrid cloud delivery experience.

* Familiarity with commercetools, Contentful, or other composable commerce/content platforms.

* Exposure to Salesforce Marketing Cloud data connectors or CDPs.

* Knowledge of Generative AI / LLM integration patterns — Vertex AI GenAI Studio, Gemini APIs.

* Experience with Databricks Agent Framework or LangChain for agentic AI workloads.

* Apache Kafka or Confluent Cloud for event streaming architectures.

* Google Cloud Professional Machine Learning Engineer.

* HashiCorp Terraform Associate.

* Experience in luxury, retail, or FMCG verticals — understanding of product data models and PIM systems.