Lead Data Modeler - Informatica/ETL

Position Overview : Analytics Data modeler Mandatory Skills : Very Strong Informatica , SQL Server and Alteryx . Good to have : Python, Exasol, MongoDB, Redis . Roles and Responsibilities : 1. Data Modeling & Design : - Lead and manage the design and development of data models for analytics and business intelligence solutions. - Create logical and physical data models for large-scale data warehousing projects, ensuring scalability, efficiency, and alignment with business requirements. - Develop dimensional models (star schema, snowflake schema) and implement best practices in data architecture, ensuring high-quality and easily maintainable data structures. - Collaborate with stakeholders to understand business requirements and translate them into effective data models. 2. Data Integration & ETL Processes : - Lead the development and optimization of ETL processes using Informatica, ensuring efficient data extraction, transformation, and loading into the target systems. - Work with SQL Server and other databases to build and manage data pipelines that support high-volume, real-time data processing. - Utilize Alteryx to automate workflows, perform data transformations, and enhance data quality. - Identify opportunities to improve data integration processes, optimize performance, and reduce ETL processing times. 3. Database and Cloud Management : - Oversee database management and optimization for the analytics platform, ensuring high performance, availability, and scalability. - Work with modern database technologies such as SQL Server, Exasol, MongoDB, and Redis to design and implement efficient data storage and retrieval solutions. - Ensure that data is correctly indexed, partitioned, and optimized to meet the needs of analytical queries. 4. Analytics & Reporting : - Develop analytics models that support business intelligence and reporting systems, enabling stakeholders to gain insights from data. - Implement data governance processes to ensure data quality, consistency, and integrity across reporting and analytical tools. - Collaborate with data scientists, analysts, and business users to understand requirements and deliver actionable insights. 5. Technical Leadership & Mentorship : - Lead a team of data modelers and developers, providing technical guidance, support, and mentorship to junior team members. - Act as the primary point of contact for technical decisions and solutions related to data modeling and analytics architecture. - Foster a collaborative environment, ensuring the team is motivated, focused, and aligned with project goals. 6. Client Interaction & Requirement Gathering : - Engage directly with clients to understand business requirements, pain points, and deliverables, ensuring that solutions meet expectations. - Conduct workshops, meetings, and presentations to discuss data modeling strategies, system design, and progress updates with clients. - Present complex technical concepts in a clear and concise manner to both technical and non-technical stakeholders. 7. Performance Optimization : - Continuously monitor and optimize the performance of data models, ETL processes, and database queries to ensure high-speed data access and reporting. - Conduct performance tuning of SQL queries, data loads, and transformation processes to reduce latency and improve efficiency. - Identify bottlenecks in the system and work with the team to implement effective solutions. 8. Documentation & Best Practices : - Maintain comprehensive documentation for data models, ETL workflows, and system configurations. - Establish and enforce best practices for data modeling, ETL development, and analytics processes, ensuring consistency and quality across projects. - Create and update standard operating procedures (SOPs) for various stages of the data modeling lifecycle. 9. Research & Continuous Learning : - Stay updated with the latest trends, tools, and technologies in data modeling, analytics, and cloud solutions. - Proactively explore new technologies such as Python, Exasol, MongoDB, and Redis to enhance the data modeling and analytics capabilities. - Promote continuous learning within the team, ensuring they are equipped with the skills and knowledge to tackle evolving business challenges. 10. Cross-functional Collaboration : - Collaborate with teams across the organization, including data engineering, application development, business intelligence, and data science, to ensure seamless data flows and integration. - Work with cloud infrastructure teams to implement scalable and efficient data storage and processing solutions in cloud environment (ref:hirist.tech)

Location: in, IN

Posted Date: 1/19/2025