Technical Business Analyst

3 weeks ago

Jersey City, United States Lorven Technologies Full time
Hi,

Our client is lookingTechnical Business Analyst in Jersey City, NJ - Hybrid below is the detailed requirements.

Job Title : Technical Business Analyst

Location : Jersey City, NJ - Hybrid

Duration : Contract

Job Description:

We are seeking an experienced Technical Business Analyst with a solid understanding of Insurance P&C (Property & Casualty), Data Modeling, Data Vaulting, and Data Governance to join our growing team. In this role, you will help define requirements, assist with data management and modeling, and ensure the governance and quality of the data across various platforms. You will collaborate with both technical and business teams to ensure seamless delivery of data solutions.

Experience:
• Minimum of 5+ years of experience in data projects including ETL, data modeling, data management, and data governance.
• Proven experience as a business analyst in a technical environment, with a strong understanding of the insurance P&C domain.
• Data Modeling expertise, including dimensional modeling and data vaulting methodologies.
• Experience with Data Governance, ensuring the accuracy, consistency, security, and availability of data.
• Hands-on experience with SQL and Python/Pyspark for data analysis across multiple source systems and formats.
• Knowledge of Insurance P&C industry standards and practices, including risk, claims, underwriting, and policy data management.
• Experience working with external data vendors/suppliers and understanding of their contractual documents and open-source data libraries.
• Strong understanding of data vaulting techniques for building scalable, flexible data architectures.
• Experience in managing and governing data assets, including master data management, metadata management, and data quality.
• Project Management experience is a plus.

Primary Skills:
• Business analysis, requirement gathering and elicitation, understanding of ETL, data modeling, and data governance.
• Hands-on experience in SQL, Python/Pyspark, and working with large datasets across various systems.
• Data Literacy, with a conceptual understanding of Data Modeling, Data Warehousing, Dimensional Modeling, and Data Vaulting.
• Experience with the Insurance P&C domain, including a deep understanding of risk and claims data, policy lifecycle, and actuarial data.
• Experience with Data Governance, ensuring data quality and consistency across systems.
• Experience working with external data vendors and understanding contractual documents, including open-source libraries (e.g., Creative Commons).
• Familiarity with RFPs (Request for Proposals), managing delivery, stakeholder management, and coordinating assessments.
• Data background with FS (Financial Services) and ESG (Environmental, Social, and Governance) data domain knowledge is a plus.
• Proficiency with typical tooling such as Jira, Confluence, and project lifecycle tools.
• Solid experience working within an Agile environment, with a focus on user stories, acceptance criteria, and sprint planning.
• Advanced MS Office Suite skills, including PowerPoint, Excel, Access, and Visio.

Responsibilities:
• Lead and manage data-related business analysis, including gathering and documenting business requirements for ETL and data modeling initiatives.
• Collaborate with technical teams to design and implement data governance processes and ensure compliance with industry standards.
• Perform data analysis and modeling tasks using SQL, Python, and Pyspark to extract and manipulate data from various systems.
• Work with business stakeholders and external vendors to ensure accurate data integration and management across platforms.
• Oversee data vaulting techniques to build scalable, flexible data solutions, ensuring long-term value.
• Participate in Agile ceremonies and support the project lifecycle, from planning through execution and delivery.
• Assist in managing external data sources and understanding data vendor terms, conditions, and licensing requirements.
• Ensure data governance practices are implemented, maintaining data accuracy, security, and consistency.

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