Hitindraa Siingh

• Collaborated with business stakeholders to gather and document functional and technical requirements for various insurance products, focusing on auto, health, and life insurance lines.
• Conducted business process analysis, identifying bottlenecks in claims processing and underwriting workflows, which led to a 10% improvement in efficiency.
• Confluence Environment Management:

Maintain and create comprehensive Business Requirement Documents (BRDs), technical specifications, and other critical documentation in Confluence, ensuring that key project files are well-organized and accessible to all stakeholders.

• Database Management with SSMS:

Utilize SQL Server Management Studio (SSMS) for querying, managing databases, and ensuring the integrity of data across various servers.

• JIRA Environment:

Effectively manage tasks and project workflows in JIRA, tracking issues, sprint progress, and project milestones to ensure timely delivery.

• Production Support & Reporting:

Provide monthly production support by generating detailed reports, analyzing performance metrics, and resolving any discrepancies or errors in the system.

• SQL Configuration Table Management:

Manage and update configuration tables in SQL to ensure accurate data flow across various platforms, supporting seamless operations.

• Automation and Process Improvement:

Lead efforts in requirement gathering for automating manual reports in production support, streamlining processes to enhance efficiency, and reduce manual workload.

• Data Mapping and Analysis:

Conduct thorough data mapping exercises, collaborating with business users to translate their requirements into actionable insights and database solutions.

• Carrier Feed Reports (Auto BDX and Manual BDX):

Oversee the generation and submission of both Auto BDX and Manual BDX carrier feed reports, ensuring data accuracy and compliance with business requirements.

• Error Summary Reporting:

Prepare weekly error summary reports for carrier feed processes, identify root causes, and work on rectifying errors to ensure consistent data integrity.

• Data Collection & Gathering:Extract, gather, and compile data from various sources such as databases, APIs, and data warehouses.
  Ensure that data is accurate, relevant, and complete for analysis.
  Work with stakeholders to identify key metrics and data points for business needs.
  
• Data Cleaning & Preprocessing: Clean raw data by removing duplicates, correcting errors, and filling in missing values.
  Standardize data formats and ensure consistency across datasets.
  Prepare datasets for analysis, ensuring they are well-organized and structured.
  
• Data Analysis & Interpretation:Analyze large datasets using statistical techniques to identify trends, patterns, and correlations.
  Use tools like SQL, Excel, Python, R, and statistical software to perform data manipulation and analysis.
  Interpret the results of the data analysis and turn raw data into actionable insights.
  
• Data Visualization & Reporting: Create visual representations of data, such as graphs, charts, and dashboards, using tools like Tableau, Power BI, or Excel.
  Develop easy-to-understand reports and presentations to communicate insights to non-technical stakeholders.
  Regularly update and maintain dashboards for real-time data monitoring.
  
• Data-Driven Decision Making: Support management and other teams (e.g., marketing, sales, finance) by providing data-driven recommendations.
  Use historical data to forecast future trends and help businesses make strategic decisions.
  Assist in scenario modeling and what-if analyses to guide decision making.
  
• Collaboration with Cross-Functional Teams: Work closely with business analysts, data scientists, IT, and other departments to ensure alignment on data needs and analysis.
  Collaborate with teams to understand their data requirements and tailor analysis to meet specific business goals.
  
• Data Quality & Integrity:Monitor data quality and ensure that data meets the required standards for accuracy and reliability.
  Identify inconsistencies or anomalies in data and take corrective actions to maintain data integrity.
  
• Ad-hoc Analysis & Problem Solving: Perform ad-hoc analysis based on specific business questions, providing immediate insights when needed.
  Assist in troubleshooting data issues and finding innovative solutions to complex data problems.
  
• Automation & Process Improvement:Automate data extraction, transformation, and reporting processes using scripts and tools like Python, SQL, and Excel VBA.
  Continuously improve data processes and workflows to optimize efficiency and reduce manual effort.
  

Requirements Gathering & Documentation:

• Work closely with stakeholders (internal and external) to understand and document business requirements, functional specifications, and business workflows.
• Translate business requirements into clear and detailed documentation for technical teams.

Process Analysis & Improvement:

• Analyze existing business processes and identify areas for improvement or automation to enhance efficiency, reduce costs, and improve service delivery.
• Conduct gap analysis between current and desired business processes.

Solution Design & Implementation:

• Collaborate with technical teams to design solutions that meet business needs. This could involve selecting software, designing workflows, or enhancing current systems.
• Facilitate communication between business stakeholders and IT teams during the development and implementation phases.

Testing & User Acceptance:

• Participate in or oversee User Acceptance Testing (UAT) to ensure that the developed solutions meet business requirements.
• Provide training and support to end-users during system implementation and post-launch.

Reporting & Presentations:

• Present findings, business cases, and recommendations to stakeholders through reports, presentations, and dashboards.

Key Responsibilities:

• Produced monthly reports using advanced Excel spreadsheet functions.
• Used statistical methods to analyze data and generate useful business reports.
• Improved decision-making processes with accurate data analysis and visualization techniques.
• Utilized data visualization tools to effectively communicate business insights.

• Enhanced team collaboration by providing clear communication of complex findings through visualizations and reports.
• Collaborated with cross-functional teams to identify opportunities for process improvement and increased efficiency.
• Improved internal knowledge sharing by developing comprehensive documentation outlining standard operating procedures for various tasks.
• Increased efficiency by streamlining data analysis processes and implementing automation tools.
• Enhanced customer satisfaction rates through detailed analysis of customer feedback data and subsequent improvements in service offerings.
• Implemented predictive analytics techniques that enabled proactive adjustments to business strategies based on emerging trends.