MPower Plus
Senior Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior Data Engineer with a contract length of "unknown", offering a pay rate of "unknown". Key skills include ETL/ELT pipeline development, cloud data engineering (AWS, Azure, GCP), and leadership experience.
🌎 - Country
United Kingdom
💱 - Currency
£ GBP
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💰 - Day rate
Unknown
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🗓️ - Date
August 13, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Greater Bristol Area, United Kingdom
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🧠 - Skills detailed
#Vault #Data Quality #Security #Terraform #Batch #BigQuery #GIT #Data Vault #Talend #dbt (data build tool) #Scala #Compliance #Datasets #Synapse #Data Engineering #PySpark #Kafka (Apache Kafka) #GCP (Google Cloud Platform) #Docker #Airflow #Informatica #Redshift #Spark (Apache Spark) #DataStage #AWS (Amazon Web Services) #SQL (Structured Query Language) #"ETL (Extract #Transform #Load)" #Infrastructure as Code (IaC) #Kubernetes #Python #Leadership #Snowflake #Azure #Cloud
Role description
Must Have Skills
• Senior data engineering leadership across modernisation, transformation, governance and analytics consumption.
• Scalable ETL/ELT pipeline development for structured, semi-structured and unstructured data.
• Batch and real-time processing using SQL, Python, PySpark/Spark and Kafka, Pub/Sub or Event Hub.
• Data modelling and analytics-ready datasets using Star Schema, Snowflake or Data Vault approaches.
• Hands-on cloud data engineering in AWS, Azure or GCP, with platforms such as BigQuery, Snowflake, Synapse or Redshift.
• Workflow/orchestration experience with Airflow, DataStage, Informatica, Talend or dbt.
• Data quality, governance, lineage, security and compliance ownership.
• CI/CD, Infrastructure as Code and container/platform tooling: Git, Terraform, Docker and Kubernetes.
• Leadership of engineers, architecture reviews, technical governance and platform optimisation.
Must Have Skills
• Senior data engineering leadership across modernisation, transformation, governance and analytics consumption.
• Scalable ETL/ELT pipeline development for structured, semi-structured and unstructured data.
• Batch and real-time processing using SQL, Python, PySpark/Spark and Kafka, Pub/Sub or Event Hub.
• Data modelling and analytics-ready datasets using Star Schema, Snowflake or Data Vault approaches.
• Hands-on cloud data engineering in AWS, Azure or GCP, with platforms such as BigQuery, Snowflake, Synapse or Redshift.
• Workflow/orchestration experience with Airflow, DataStage, Informatica, Talend or dbt.
• Data quality, governance, lineage, security and compliance ownership.
• CI/CD, Infrastructure as Code and container/platform tooling: Git, Terraform, Docker and Kubernetes.
• Leadership of engineers, architecture reviews, technical governance and platform optimisation.
