the design, build, and maintain scalable data pipelines using Azure Data Factory and Databricks to automate data ingestion, transformation, and processing workflows. DCreate and maintain dimensional data models and semantic layers that support business intelligence and analytics use cases. Build and optimise data transformation workflows using dbt, SQL, and Python to create clean, well-documented, and version-controlled analytics More ❯
Leeds, West Yorkshire, Yorkshire, United Kingdom Hybrid/Remote Options
Fruition Group
learning, and self-service insights. Senior Analytics Engineer/Data Modeller Responsibilities Design, build, and maintain analytical and AI-ready data marts sourced from the enterprise data lakehouse. Develop semantic models and dimensional structures optimised for BI, dashboarding, and machine learning. Ensure documentation, governance, and data consistency across domains. Collaborate with data engineers to support robust ETL/ELT … and hands-on experience with platforms such as Snowflake, Azure Synapse, BigQuery, or Redshift. Deep understanding of Data Vault, Kimball, and dimensional modelling techniques. Experience designing data marts and semantic layers for BI tools (Power BI, Tableau, Looker). Familiarity with analytics engineering tools including dbt, Airflow, and Git for version control. Excellent collaboration and communication skills, with strong More ❯
using protocols like OPC UA, MQTT, REST. Design and manage data lakes, warehouses, and streaming platforms for predictive analytics, digital twins, and operational intelligence. Define and maintain asset hierarchies, semantic models, and metadata frameworks for contextualized industrial data. Implement CI/CD pipelines for data workflows and ensure lineage, observability, and compliance across environments. Collaborate with AI/ML … Apache Airflow, Glue, Kafka, Informatica, EventBridge etc. Industrial Data Integration:Familiarity with OT data schema originating from OSIsoft PI, SCADA, MES, and Historian systems. Information Modeling:Experience in defining semantic layers, asset hierarchies, and contextual models. Data Governance:Hands-on experience Data Quality:Ability to implement profiling, cleansing, standardization, and anomaly detection frameworks. Security & Compliance:Knowledge of data privacy More ❯
