***YOU MUST HAVE A VALID RIGHT TO WORK & LOCATED IN THE UK TO BE CONSIDERED FOR THE POSITION***
(No relocation or visa sponsorship is provided).
OVERVIEW:
CluePoints provides best-in-class AI-driven SaaS solutions to enable the Life Sciences industry to focus on what matters most. Our products enable both risk planning and operational risk monitoring, and utilize advanced statistical algorithms to determine the quality of clinical trial data both during and post study execution.
If you're an ambitious and driven professional looking to take your career to the next level, this is the perfect opportunity to join a dynamic and fast-growing company in the exciting e-clinical space. Apply now and help us revolutionize the clinical research industry!
SCOPE:
The Senior Data Engineer will play a crucial role within the Data & Analytics team, driving the development and implementation of a centralised Data Warehouse infrastructure. This position involves architecting, designing, and delivering scalable data solutions to streamline and optimise data storage, integration, and management processes. Leveraging expertise in the Kimball Methodology and Azure Data Factory, the Senior Data Engineer will ensure seamless integration of data from diverse sources, maintaining consistency, accuracy, and reliability.
In addition to these responsibilities, the Senior Data Engineer will design and develop robust KPI dashboards to meet the needs of various stakeholders. They will work closely with Senior Data Analysts to provide well-aggregated and curated datasets, ensuring readiness for reporting and analytics purposes. This collaborative approach is essential to deliver high-quality insights and support data-driven decision-making across the organisation.
RESPONSIBILITIES:
Lead the design and implementation of robust and scalable data architecture solutions on the Azure platform, ensuring optimal performance and efficiency.
Drive the implementation of data integration processes to seamlessly ingest data from diverse sources into Azure SQL Server, leveraging Azure Data Factory to design and develop ETL processes efficiently.
Champion the maintenance of data quality, reliability, and integrity within Azure-stored datasets by enforcing data governance policies, conducting QA checks, and establishing effective monitoring mechanisms to uphold data quality standards.
Utilize analytical skills to thoroughly review API documentation and analyze raw data and source system data models, ensuring comprehensive understanding and preparation before loading data into the staging area.
Take ownership of maintaining documentation such as data dictionaries and data models, and actively share knowledge and expertise with team members to cultivate a culture of continuous learning and improvement.
Collaborate with Senior Data Analysts to assist with complex SQL queries and ensure seamless reporting in Power BI. Act as a backup during their absence to maintain reporting continuity and meet requirements.
Demonstrate adaptability and effectiveness in working under a hybrid working model, delivering high-quality work across global teams with efficiency and professionalism.
Strong communication and presentation skills to convey technical concepts to non-technical stakeholders and build collaboration across teams.
A passionate technologist with an inherent love for data, eager to drive insights and innovation
Demonstrated mastery of SQL is essential, including proficiency in creating tables, views, stored procedures, and crafting succinct queries to populate our Power BI models effectively.
Minimum of 5 years of hands-on experience in analyzing and extracting data from diverse sources, showcasing a deep understanding of data extraction (including REST APIs) methodologies and best practices.
EXPERIENCE AND SKILLS REQUIRED:
Extensive Data Warehouse Experience: At least 7 years of hands-on experience designing, building, and managing data warehouses using the Kimball methodology.
Proficiency in Azure Tools: Demonstrated expertise in Azure Data Factory (ETL processes), Azure SQL Server, Azure DevOps, Key Vaults, and Logic Apps.
Strong Data Modeling Skills: Proven ability to design efficient and scalable data warehouse architectures, including the creation of robust staging and production layers.
Performance Optimization: Experience in optimizing data loads to ensure reliability, scalability, and high performance, with a focus on reducing processing times and improving efficiency.
Forward-Thinking Vision: A solid understanding of advanced data technologies and practices, with the ability to scale the data warehouse to support future growth and evolving business needs.
Integration Expertise: Experience integrating data from diverse sources, ensuring data accuracy and consistency throughout the ETL process.
Problem-Solving Abilities: Adept at troubleshooting and resolving complex data pipeline and infrastructure issues.
Collaboration Skills: Ability to work closely with data analysts, stakeholders, and cross-functional teams to align data solutions with business objectives.
Agile and DevOps Practices: Familiarity with CI/CD pipelines and agile workflows to streamline data warehouse development and deployment.
NICE TO HAVE (DESIRABLE):
Previous SaaS experience - working with Software as a Service (SaaS) companies
Familiriality with Microsoft Power BI to support data visualization and advanced analytics needs.
Understanding of cloud cost management strategies to ensure efficient use of Azure resources.
Proficiency in scripting languages like Python, PowerShell, or Bash for automation and orchestration tasks.
Hands-on experience with tools and practices to monitor and improve data quality in the pipeline.
SSIS experience: Proficiency in SQL Server Integration Services (SSIS) is advantageous, as it demonstrates the ability to design, implement, and manage robust data integration solutions within Microsoft SQL Server environments, complementing our data engineering initiatives.
Experience with Alteryx analytics platform is a plus, indicating expertise in data blending, advanced analytics, and workflow automation, which can enhance our data processing capabilities and accelerate insights generation.
Our current data stack includes an Enterprise Data Warehouse built using the Kimball Methodology in Azure SQL databases (production and staging), a fully developed BI solution in Power BI with over 50 reports, and multiple Alteryx reports for data blending and data quality purposes.