Analytics Engineer

Key Responsibilities

• Design, develop, and maintain low-code enabled data pipelines for collecting, transforming, and loading data into various data stores and build insightful reports.
• Build and maintain data warehousing and data lake solutions that support quick and efficient reporting and insights
• Develop and deploy data models that cater to various business requirements and enable efficient data analysis
• Lead the design of data solutions with a focus on automation, performance, and quick report generation
• Ensure data is readily available for business and analytics consumption and monitor the quality of the data
• Collaborate with cross-functional teams and stakeholders to understand data requirements and drive data-driven initiatives
• Prototype and adopt new approaches to drive innovation into the solutions and ensure they are aligned with industry trends and advancements
• Develop and implement the data roadmap for strategic data sets and communicate progress to both technical and non-technical stakeholders
• Communicate complex data solutions in a clear and understandable manner to both experts and non-experts
• Interact with stakeholders and clients to understand their data requirements and provide low-code enabled solutions
• Stay up-to-date with industry trends and technology advancements in data engineering and analytics
• Champion the importance of modern data solutions across the business and promote the value of obtaining good quality data.
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  Knowledge, Skills, and experience required
  
  
  • Extensive experience leading AWS and cloud data platform transformations
  • Proven track record of delivering large-scale data and analytical solutions in a cloud environment
  • Hands-on experience in end-to-end data pipeline implementation using AWS services, including data preparation, extraction, transformation & loading, normalization, aggregation, warehousing, data lakes, and data governance
  • Expertise in developing data warehouses and in-depth understanding of modern data architecture such as Data Lake, Data Warehouse, Lakehouse, and Data Mesh
  • Strong knowledge of data architecture and data modelling practices, with the ability to cost-effectively manage data pipelines
  • Data Modelling,Python, SQL, NoSQL DBs (Mongo), Snowflake, AWS Cloud, APIs, Tableau, PowerBI, DQ Framework, DWH, Real time reporting