Data Engineer, Assurant, GCC-India

Data Engineer reports to the Manager of Data Services and is responsible for developing Data Engineering solutions that conform to group standards, budgets and agreed estimated timelines. They will deliver data solutions in support of reporting, analytics, business intelligence, AI, machine learning, and big data.

The Data Engineer works directly with business and IT to execute specific data initiatives. They will ensure timely and accurate execution of projects using agile methodology. The Data Engineer works in collaboration with Global Data Services and follows current standards and aligns priorities with department strategy and initiatives. They must be able to communicate effectively with all levels of business and IT to manage delivery commitment and expectation.   

This position will be in Hyderabad/Bangalore/Chennai at our India location.

Work Timing = 5:00 AM IST ~ 2:00 PM IST

What will be my duties and responsibilities in this job?

• Implement Data Warehouse solutions in multiple platforms - from on premise to cloud solutions.
• Familiarity with modern enterprise data and reporting architectures, design patterns, and data toolsets.
• Support Data Services in ensuring the Data Management platform’s stability, scalability, and capacity planning to support business growth.
• Execute data consolidation and optimization projects, in collaboration with the Data Analytics team.
• Skills and capabilities to support implementation of new technologies such as AI, Machine Learning, and big data.
• Assist in evaluation and selection of data management's tools and technology to align with our technology strategy.
• Contribute to development process methodology and tools following industry best practices and Assurant’s standard, such as Agile methodology, ceremonies, and DevOps tool.
• Builds processes to ingest, process and store massive amounts of data.
• Assists with optimization the performance of bigdata ecosystems.
• Wrangles data in support of data services and reporting projects.
• Collaborates and contributes in identifying project risks, design mitigation plans, develops estimates.
• Contributes to the design, development of data-pipelines, and feature engineering of data solutions.
• Established processes or methods are still relied on, however, Data Engineer will be required to come up with creative solutions to problems. Senior teammates are still needed to provide oversight on solutions to complex problems.

What are the requirements needed for this position?

• 5+ years of experience in Data Warehouse, Business Intelligence, Analytics, or Information Management
• 3 years of experience in gathering business requirements and converting them to functional specifications
• 3 years of working experience in building large scalable data & analytics environments in either private or public (cloud) infrastructure
• 1 year of working experience with Agile delivery methodology for data & analytics products
• Understanding of Data Governance's concepts and processes related to data quality, privacy, and security
• Strong written, verbal, and interpersonal communication skills. Ability to effectively communicate at all levels in the organization.
• Bachelor’s degree in computer science, Information Management Systems, or related subjects Bachelor of Science in a related field required

What are the other requirements needed for this position?

Industry Background: Experience working in a global insurance organization.

Microsoft BI Stack Expertise: Hands-on experience with:

• SQL Server Database Engine
• Integration Services (SSIS)
• Analysis Services (SSAS)
• Reporting Services (SSRS)
• Power BI, Power Apps, and PowerShell

Cloud Platforms: Practical experience with Azure and AWS for data integration and migration.

Development Methodologies: Familiarity with Agile and Waterfall approaches. CI/CD with Azure DevOps.

Big Data Concepts: Understanding of Big Data technologies and their application in data processing.

Business Intelligence & Data Warehousing: Advanced knowledge of BI principles and data warehouse architecture.

Machine Learning & AI Fundamentals: Basic understanding of ML and AI concepts using Microsoft technologies.

SQL Performance Optimization: Strong skills in performance tuning and query optimization.

Power BI Architecture & Performance: Expertise in:

• Semantic modeling
• Incremental refresh configuration
• DAX optimization and tuning

Data Modeling: Proficiency in:

• Data profiling
• Dimensional modeling techniques
• Creating logical and physical data models