oracle
Assistant Manager - Data Analyst
KPMG Assurance and Consulting Services LLP
Noida, IND
Full-time
Mid
Onsite
Discovered 3 weeks ago
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About KPMG in India
KPMG entities in India are professional services firm(s). These Indian member firms are affiliated with KPMG International Limited. KPMG was established in India in August 1993. Our professionals leverage the global network of firms, and are conversant with local laws, regulations, markets and competition. KPMG has offices across India in Ahmedabad, Bengaluru, Chandigarh, Chennai, Gurugram, Jaipur, Hyderabad, Jaipur, Kochi, Kolkata, Mumbai, Noida, Pune, Vadodara and Vijayawada.
KPMG entities in India offer services to national and international clients in India across sectors. We strive to provide rapid, performance-based, industry-focused and technology-enabled services, which reflect a shared knowledge of global and local industries and our experience of the Indian business environment.
Functional Skills
- Be responsible for the development of the conceptual, logical, and physical data models, oversight of the implementation of RDBMS, operational data stores (ODS), application databases, data marts, and data lakes on target platforms (SQL/NoSQL/cloud/on-prem) using Erwin or similar.
- Oversee and govern the expansion of existing data architecture and the optimization of data query performance via best practices. The candidate must be able to work independently and collaboratively.
- Candidate should be an expert in Data Engineer skills including Python, PySpark and SQL.
- Candidate should have in-depth understanding of BFSI domain [Banking Finance]
- Implement business and IT data requirements through new data strategies and designs across all data platforms (application, relational, dimensional, and NoSQL) and data tools (reporting, visualization, analytics, operations and machine learning).
- Work with business and application/solution teams to implement data strategies, build data flows, and develop conceptual/logical/physical data models
- Define and govern data modelling and design standards, tools, best practices, and related development for enterprise data models.
- Identify the architecture, infrastructure, and interfaces to data sources, tools supporting automated data loads, security concerns, analytic models, and data visualisations
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