Senior Reporting and Analytics Engineer II
Marriott Tech Accelerator
- Hyderabad, Telangana
- Permanent
- Full-time
- Design, build and maintain reports and dashboards to visualize procurement data within the procurement source-to-pay platform or external BI tools (like Tableau or Power BI).
- Develop tailored reports and interactive dashboards using the built-in reporting features in the procurement source-to-pay platform
- Design and optimize data models and data structures
- Define and design data flow, data extraction, data cleaning and extraction solutions
- Collaborate with business stakeholders to understand reporting needs and to define key performance indicators (KPIs)
- Serve as a subject matter expert on built-in analytics features in the procurement source-to-pay platform, including out-of-box reports, customizable dashboards,and the ability to drill down into operational data.
- Serve as the lead data and analytics engineer for procurement technology
- Collaborate with technical and business teams to analyze procurement processes, identify areas for improvement, and recommend changes to enhance efficiency and effectiveness within the procurement platform
- Analyze data to identify trends, patterns, and anomalies related to procurement activities, such as spend by category, supplier performance, cost savings, and contract compliance.
- Support the procurement business to make data-driven decisions within their procurement operations, driving efficiency, cost savings and strategic sourcing
- Experience in a data or analytics engineering role, with a focus on building and maintaining reporting and analytics systems and dashboards.
- Maintain industry knowledge and enhance subject matter expertise, identify trends and changes in technology and automation strategies.
- Assist with interviewing talent, provide peer reviews/feedback frequently and foster a modern engineering culture.
- Serve in the on call rotation
- Develop specific goals and plans to prioritize, organize, and accomplish work
- Provide technical leadership for successful platform and project implementations
- Assist with determining priorities, schedules, plans and necessary resources to complete projects on schedule
- Assist with reviewing vendor proposals and selecting appropriate vendor for services/technologies
- Understand and meet the needs of key stakeholders
- Communicate concepts in a clear and persuasive manner that is easy to understand
- Demonstrate an understanding of business priorities
- Support achievement of performance goals, budget goals, team goals, etc.
- Perform other reasonable duties as required for this position
- Experience in a data or analytics engineering role, with a focus on building and maintaining reporting and analytics systems and dashboards.
- Strong technical skills in data manipulation, analysis, and visualization, along with experience in building and managing data pipelines and reporting systems.
- Strong communication and leadership skills to collaborate with stakeholders and guide less experienced team members. Key areas of expertise include SQL, Python, cloud data warehousing, ETL processes, and BI tools.
- Blend of technical and analytical skills to manage and interpret financial data.
- Key skills include strong SQL proficiency, experience with data modeling and warehousing, and knowledge of ETL / ELT processes.
- Familiarity with cloud platforms (AWS, Azure, GCP) and tools like dbt is also crucial. Furthermore, strong communication and problem-solving abilities are essential for collaborating with stakeholders and translating business needs into actionable insights.
- The ability to communicate technical concepts to both technical and non-technical stakeholders is important.
- Proficiency in SQL for querying and manipulating large datasets, and Python for data analysis, scripting, and automation.
- Experience with cloud platforms like AWS, Azure, or Google Cloud and their respective data warehousing solutions.
- Experience with extract, transform, load processes and tools to build data pipelines. Familiarity with frameworks like Airflow is also beneficial.
- Understanding of dimensional modeling, fact tables, and dimension tables, and experience with tools like dbt (data build tool).
- Experience implementing data quality checks and processes to ensure accuracy and reliability.
- Experience with big data technologies like Spark, Presto, or Hadoop is a plus.
- Experience with Large Language Models (LLMs) and related techniques like prompt engineering and fine-tuning.
- Solid understanding of various machine learning paradigms and practical experience deploying them.
- Knowledge of reasoning frameworks, agent architectures, and Retrieval-Augmented Generation (RAG).
- Undergraduate degree in an engineering or computer science discipline and/or equivalent experience / certification.