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Data Engineer

2 months ago

London, Greater London, United Kingdom Canada Life Full time
About the Role

We are seeking a highly skilled Data Engineer to join our team at Canada Life. As a Data Engineer, you will play a crucial part in driving data-driven decision-making across the organization, ensuring data availability, quality, and accessibility for various business needs.

Key Responsibilities
  • Data Pipeline Development
    • Design, model, develop, and maintain data pipelines to ingest, store, process, and present data.
    • Ensure data quality, accuracy, and consistency.
    • Collaborate with data architects to ensure data pipelines align with the overall data architecture strategy.
  • Data Transformation and Integration
    • Perform data transformation tasks, including data cleansing, enrichment, and aggregation, to prepare data for analytics and reporting.
    • Integrate data from structured and unstructured sources, ensuring compatibility and alignment with data models and business requirements.
    • Automate data transformation processes to improve efficiency.
  • Data Quality Assurance
    • Implement and maintain data quality checks and validation processes to identify and resolve data anomalies and errors.
    • Monitor data pipelines for data quality issues and implement data quality improvements.
    • Collaborate with business stakeholders to define data quality requirements.
  • Data Modelling and Schema Design
    • Collaborate with data architects and data scientists to design and implement data models, schemas, and structures.
    • Ensure that data models support business reporting and analytics needs while optimizing query performance.
    • Maintain data dictionaries and metadata to document data structures and relationships.
  • Performance Optimization
    • Optimize data storage, retrieval, and query performance by implementing indexing, partitioning, and caching strategies.
    • Monitor data processing performance and address bottlenecks as they arise.
    • Stay updated with best practices in data processing performance tuning.
  • Documentation and Knowledge Sharing
    • Create and maintain documentation for data pipelines, data transformation processes, and data integration procedures.
    • Foster a culture of knowledge sharing within the data engineering team and across the organization.
  • Collaboration and Stakeholder Engagement
    • Collaborate effectively with cross-functional teams, data stakeholders, and business units to understand data requirements and deliver data solutions that meet business needs.
    • Communicate technical concepts and data solutions to non-technical stakeholders in a clear and understandable manner.