01Key Responsibilities
Data Architecture and Design:
Design scalable, reliable, and secure data solutions that meet real business needs
Develop data models, data flows, and integration architectures from the ground up
Own the full lifecycle from requirements gathering to production deployment
Ensure solutions meet business requirements and align with best practices.
Data Ingestion and Integration
Build and manage data pipelines to ingest data from various sources, including third-party and RESTful APIs.
Build ETL/ELT processes using ADF, Fabric Data Flows, and Notebooks.
Embed MDM solutions into pipelines, ensuring seamless data sync across systems.
Data Storage and Management
Design and implement data storage solutions using Microsoft Fabric, Azure SQL Database, and Azure Delta Lake.
Define match, merge, and survivorship rules for master data management and ensure platform integration with other systems.
Data Transformation and Processing
Use Python, PySpark, and Fabric Notebooks to process and transform data at scale.
Implement transformation logic to prepare data for analysis and reporting.
Optimize data processing workflows for efficiency and scalability.
Analytics and Reporting
Design and develop SSAS OLAP cubes, tabular & semantic models to support complex analytical queries and reporting.
Define dimensions, hierarchies, and measures; write MDX and DAX queries to retrieve and manipulate data.
Integrate models with reporting tools such as Power BI, Excel, etc. to deliver dashboards and insights.
Data Quality
Implement data quality checks, validation processes, and monitoring to ensure accuracy and consistency.
Maintain compliance with data governance policies across the full data lifecycle.
Monitoring and Optimization
Monitor data solutions for performance, reliability, and scalability; troubleshoot and resolve issues promptly.
Optimize query and price performance through indexing and partitioning strategies.
Security
Implement role-based access control, auditing, and encryption to protect data assets.
Collaboration and Communication
Work closely with analysts and business stakeholders to understand data requirements.
Collaborate with cross-functional teams to integrate data solutions with other systems and applications.
Documentation and Training
Document data architectures, processes, and procedures.
Provide training and support to team members and end-users.
Other duties as assigned.
Qualifications:
Core Skills:
Python + SQL: Strong Python and SQL proficiency, including advanced queries and query optimization.
Big Data: Apache Spark / PySpark for large-scale distributed data processing.
Data Pipelines: Building ETL/ELT pipelines using Azure Data Factory, Fabric Data Flows, and Fabric Notebooks.
Cloud + Warehouse: Hands-on experience with Microsoft Azure services and Microsoft Fabric for cloud data warehousing and lakehouse architecture.
Additional Required Experience:
Demonstrated experience analyzing complex business and technical needs to develop data architecture and solutions.
Excellent communication skills to articulate complex data concepts to diverse stakeholders.
Strategic thinker with strong problem-solving and analytical skills.
Data security and privacy compliance: role-based access control, identity management, auditing, encryption, and API security best practices.
Data quality management, monitoring, and full data lifecycle management.
Familiarity with SSAS for OLAP cube and tabular model development.
Experience integrating streaming data and RESTful API sources into data pipelines.
Minimum 5 years of experience in database development, data architecture, or data engineering.
Bachelor's or master's degree in computer science, Data Science, Information Technology, or related field (or equivalent experience).
Certifications in Azure, Microsoft Fabric, or related areas are desirable.
Project management experience is a plus.
Bonus / Nice-to-Have:
Power BI for dashboard development and self-service reporting.
Data modeling best practices and advanced performance tuning techniques.
API management tools such as Azure API Management, Postman, or Swagger.
Master data management using Profisee software.
Data estate governance using Microsoft Purview for data quality monitoring.
Containerization and orchestration tools for deployment.
Experience with AirByte connectors and dbt (data build tool).
Familiarity with accounting principles and the financial services industry.
Basic AI/ML concepts .
