Posted:4 hours ago| Platform: Linkedin logo

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On-site

Job Type

Full Time

Job Description

Project Role :

Application Lead

Project Role Description :

Lead the effort to design, build and configure applications, acting as the primary point of contact.

Must have skills :

Data Modeling Techniques and Methodologies

Good to have skills :

NA

Minimum 5 Year(s) Of Experience Is Required

Educational Qualification :

15 years full time educationSummary: As a Senior Data Infrastructure Engineer, you will be responsible for designing and supporting infrastructure development across multiple business initiatives.
  • Designing Data Models: Develop logical and physical data models tailored to business requirements, optimizing for both analytical and operational use cases. This includes normalized and denormalized schemas to accommodate diverse needs from reporting to transactional processing.
  • Data Architecture Development: Collaborate with architects and engineers to define robust data architecture, ensuring compatibility with existing infrastructure and scalability for future growth.
  • SQL and Advanced Data Modeling: Leverage expert-level SQL skills to craft efficient queries, optimize data retrieval, and support complex joins, aggregations, and calculations integral to business intelligence and analytics.
  • Performance Optimization: Tune database systems to enhance performance, reduce latency, and maximize throughput. Identify bottlenecks in data processing and implement strategies for continuous improvement.
  • Collaboration: Work closely with product managers, business analysts, and application developers to translate business requirements into scalable, maintainable data solutions. Participate in Agile teams and act as a bridge between technical and non-technical stakeholders.
  • Establishing Data Warehousing Infrastructure: Architect and maintain data warehouse solutions that provide reliable storage, rapid access, and secure management of large data sets. Ensure infrastructure meets scalability and high availability demands.
  • Metric and KPI Development: Define and implement key performance indicators (KPIs) and metrics that deliver actionable insights. Enable cost savings and performance optimizations for technical and product teams by deriving meaningful measures from raw data.
  • Data Governance and Quality Assurance: Uphold best practices in data governance, including metadata management, data lineage tracking, and enforcing standards in data integrity, consistency, and security.
  • Documentation: Maintain detailed documentation of data models, schemas, and business logic to ensure transparency and facilitate knowledge transfer across teams.
  • Reporting and Visualization: Build dashboards and reporting solutions for both technical and business audiences, enabling real-time visibility into data performance, user activity, and trends.
  • Support and Troubleshooting: Act as a subject matter expert to support 24/7 operations, troubleshooting complex data issues and providing solutions that maintain uninterrupted business processes.

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