Lead Data Modeler - Informatica/ETL (12-15 yrs)

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