Lead Data Analytics

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

·        8+ 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, Superset, Tablue 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.