Bachelor's or Master's degree in Computer Science, Information Systems, Data Analytics, or a related field.
Strong understanding of end-to-end data warehousing concepts, including data modeling, ETL/ELT, and data integration.
Proficient in Azure Synapse, Databricks, Azure Data Factory, Data bricks, Snowflake and Power BI for modern cloud-based data solutions.
Skilled in handling structured, semi-structured (JSON, XML), and unstructured data across Azure and AWS platforms.
Expertise in Power Query and Power M for advanced data transformation and reporting automation.
Solid grasp of Agile methodologies, functional requirement gathering, and stakeholder communication.
Familiar with metadata management, data lineage, and data quality principles to ensure trusted analytics.
Experience
10+ years of experience in designing, implementing, and optimizing end-to-end data warehouse solutions across cloud and on-premise ecosystems.
Orchestrated end-to-end enterprise DWH solutions using Azure Synapse, Databricks, and ADF, integrating structured and semi-structured data sources while ensuring lineage, auditability, and performance at scale.
Translated ambiguous business needs into scalable dimensional models and Power BI solutions, leveraging advanced Power Query (M) scripting and DAX for dynamic, self-service analytics.
Designed and governed hybrid data pipelines across Azure and AWS platforms, ensuring data consistency, compliance, and traceability from ingestion to visualization.
Collaborated with business stakeholders and engineering teams to define functional KPIs, automate reconciliations, and embed validation rules across ETL and reporting layers.
Led multi-cloud integration of disparate systems into a unified analytics layer, optimizing transformations through Databricks notebooks, ADF workflows, and parameterized datasets.
Enabled real-time insights by architecting incremental refresh logic in Power BI and optimizing semantic models with calculated measures, role-level security, and composite models.
Implemented data observability frameworks across pipelines, leveraging metadata-driven controls to proactively detect anomalies and ensure SLA adherence across reporting layers.
