Data Analytics Lead

Responsibilities

• Oversee the design, development, and implementation of data analysis solutions to meet business needs.
• Work closely with business stakeholders and the Aviation SME to define data requirements, project scope, and deliverables.
• Drive the design and development of analytics data models and data warehouse designs.
• Develop and maintain data quality standards and procedures.
• Manage and prioritize data analysis projects, ensuring timely completion.
• Identify opportunities to improve data analysis processes and tools.
• Collaborate with Data Engineers and Data Architects to ensure data solutions align with the overall data platform architecture.
• Evaluate and recommend new data analysis tools and technologies.
• Contribute to the development of best practices for data analysis.
• Participate in project meetings and provide input on data-related issues, risks and requirements.
  

Qualifications

• 12+ years of experience as a Data Analytics Lead, with experience leading or mentoring a team.
• Extensive experience with cloud-based data modelling and data warehousing solutions, using Azure Data Bricks.
• Proven experience in data technologies and platforms, ETL processes and tools, preferably using Azure Data Factory, Azure Databricks (Spark), Delta Lake.
• Advanced proficiency in data visualization tools such as Power BI.
  

Data Analysis And Visualization

• Experience in data analysis, statistical modelling, and machine learning techniques.
• Proficiency in analytical tools like Python, R, and libraries such as Pandas, NumPy for data analysis and modelling.
• Strong expertise in Power BI for data visualization, data modelling, and DAX queries, with knowledge of best practices.
• Experience in implementing Row-Level Security in Power BI.
• Ability to work with medium-complex data models and quickly understand application data design and processes.
• Familiar with industry best practices for Power BI and experienced in performance optimization of existing implementations.
• Understanding of machine learning algorithms, including supervised, unsupervised, and deep learning techniques.
  

Data Handling And Processing

• Proficient in SQL Server and query optimization.
• Expertise in application data design and process management.
• Extensive knowledge of data modelling.
• Hands-on experience with Azure Data Factory, Azure Databricks.
• Expertise in data warehouse development, including experience with SSIS (SQL Server Integration Services) and SSAS (SQL Server Analysis Services).
• Proficiency in ETL processes (data extraction, transformation, and loading), including data cleaning and normalization.
• Familiarity with big data technologies (e.g., Hadoop, Spark, Kafka) for large-scale data processing.
• Understanding of data governance, compliance, and security measures within Azure environments.