Solution Architect

Licensed sponsor United Kingdom Posted 11 hours ago

Design, develop, test, deploy, and maintain scalable ETL/ELT pipelines for structured and unstructured data.
Build batch and real-time processing solutions using BigQuery, Dataflow, Cloud Storage, Pub/Sub, and related GCP services.
Develop data-processing components and integrations using Java or Python, SQL, REST APIs, Kafka, and containerized services on GKE.
Implement reusable ingestion and transformation frameworks supporting full, incremental, and event-driven loads.
Design data models and optimize BigQuery tables through partitioning, clustering, efficient SQL, and cost-aware processing patterns.
Produce high-level and low-level technical designs, document assumptions, and contribute to architecture reviews.
Implement data-quality checks, schema validation, reconciliation, audit logging, lineage, monitoring, alerting, and error-handling mechanisms.
Troubleshoot complex production issues, perform root-cause analysis, and deliver sustainable corrective actions.
Apply security, privacy, access-control, reliability, and operational-support standards across data solutions.
Use source control, automated testing, CI/CD, and infrastructure-as-code practices to promote repeatable deployments.
Participate in code reviews, improve engineering standards, and mentor less-experienced engineers.
Collaborate with architects, analysts, application teams, platform teams, and business stakeholders to deliver reliable data products.

Strong hands-on experience with GCP data services, particularly BigQuery, Dataflow, Cloud Storage, and Pub/Sub.
Proficiency in SQL, including complex transformations, query tuning, data validation, and analytical processing.
Proficiency in Java or Python for data pipelines, automation, API integration, and production support.
Good understanding of ETL/ELT, data warehousing, data lakes, dimensional modelling, batch processing, and stream processing.
Experience with Apache Beam, Kafka, REST APIs, Docker, Kubernetes, or GKE.
Experience with data formats such as JSON, CSV, Avro, and Parquet.
Working knowledge of Git, automated testing, CI/CD pipelines, observability, and release management.
Ability to design solutions for scalability, resilience, security, performance, operability, and cost efficiency.

Experience with Cloud Composer or Apache Airflow, Dataproc or Spark, Dataform or dbt, and metadata-driven pipeline frameworks.
Exposure to Terraform or another infrastructure-as-code tool.
Knowledge of Dataplex, data governance, metadata management, lineage, and access-control practices.
Experience modernizing legacy or on-premises data workloads to GCP.
Google Cloud Professional Data Engineer certification or an equivalent cloud data certification.

Typical Qualifications

Degree in Computer Science, Software Engineering or a related discipline, or equivalent practical experience.

Experience delivering software solutions in agile teams.
Knowledge of modern development frameworks, tools and engineering practices.

Design, develop, test, deploy, and maintain scalable ETL/ELT pipelines for structured and unstructured data.
Build batch and real-time processing solutions using BigQuery, Dataflow, Cloud Storage, Pub/Sub, and related GCP services.
Develop data-processing components and integrations using Java or Python, SQL, REST APIs, Kafka, and containerized services on GKE.
Implement reusable ingestion and transformation frameworks supporting full, incremental, and event-driven loads.
Design data models and optimize BigQuery tables through partitioning, clustering, efficient SQL, and cost-aware processing patterns.
Produce high-level and low-level technical designs, document assumptions, and contribute to architecture reviews.
Implement data-quality checks, schema validation, reconciliation, audit logging, lineage, monitoring, alerting, and error-handling mechanisms.
Troubleshoot complex production issues, perform root-cause analysis, and deliver sustainable corrective actions.
Apply security, privacy, access-control, reliability, and operational-support standards across data solutions.
Use source control, automated testing, CI/CD, and infrastructure-as-code practices to promote repeatable deployments.
Participate in code reviews, improve engineering standards, and mentor less-experienced engineers.
Collaborate with architects, analysts, application teams, platform teams, and business stakeholders to deliver reliable data products.

Strong hands-on experience with GCP data services, particularly BigQuery, Dataflow, Cloud Storage, and Pub/Sub.
Proficiency in SQL, including complex transformations, query tuning, data validation, and analytical processing.
Proficiency in Java or Python for data pipelines, automation, API integration, and production support.
Good understanding of ETL/ELT, data warehousing, data lakes, dimensional modelling, batch processing, and stream processing.
Experience with Apache Beam, Kafka, REST APIs, Docker, Kubernetes, or GKE.
Experience with data formats such as JSON, CSV, Avro, and Parquet.
Working knowledge of Git, automated testing, CI/CD pipelines, observability, and release management.
Ability to design solutions for scalability, resilience, security, performance, operability, and cost efficiency.

Experience with Cloud Composer or Apache Airflow, Dataproc or Spark, Dataform or dbt, and metadata-driven pipeline frameworks.
Exposure to Terraform or another infrastructure-as-code tool.
Knowledge of Dataplex, data governance, metadata management, lineage, and access-control practices.
Experience modernizing legacy or on-premises data workloads to GCP.
Google Cloud Professional Data Engineer certification or an equivalent cloud data certification.

Typical Qualifications

Degree in Computer Science, Software Engineering or a related discipline, or equivalent practical experience.

Experience delivering software solutions in agile teams.
Knowledge of modern development frameworks, tools and engineering practices.

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