Remote Data Practise Lead

Summary

Join our team as a Data Practise Lead to design highly technical solutions, establish best practices, deliver workshops, create POCs, thought leadership, represent Encora at technical conferences within the country, work with our key partners, and provide leadership to the Practitioners in India.

Requirements

• A bachelor’s degree in computer science, information technology, or a closely related discipline is usually necessary. A master’s degree is preferable
• Prior experience in data modelling, database design, and data administration is required
• Database Expertise: Knowledge of data warehousing ideas and proficiency in various database systems (e.g., SQL, NoSQL)
• Knowledge of data modelling tools such as Erwin, or Microsoft Visio is required
• Knowledge of ETL methods and technologies (for example, Apache NiFi, Talend, and Informatica)
• Strong problem-solving and analytical skills are required to build effective data solutions
• Excellent communication skills are required to work with cross-functional teams and convert business objectives into technical solutions
• Knowledge of Data Governance: Understanding data governance principles, data security, and regulatory compliance
• Knowledge of programming languages such as Python or Java can be advantageous

Responsibilities

• Creating data models that specify how data is formatted, stored, and retrieved inside an organisation
• Creating and optimising databases, including the selection of appropriate database management systems (DBMS) and the standardisation and indexing of data
• Creating and maintaining data integration processes, ETL (Extract, Transform, Load) workflows, and data pipelines to seamlessly transport data between systems
• Collaborating with business analysts, data scientists, and other stakeholders to understand data requirements and align architecture with business objectives
• Stay current with industry trends, best practices, and advancements in data management through continuous learning and professional development
• Establishing processes for monitoring and improving the quality of data within the organisation
• Implement data quality tools and practices to detect and resolve data issues