Senior Data Engineer

You will be mainly accountable for:

• Designing and developing end-to-end scalable ETL/ELT pipelines using Snowflake (Snowpipe, Streams, Tasks, Stored Procedures), Azure Data Factory, SQL, and related Azure services to ingest, transform, and deliver robust data models and analytics-ready datasets across data layers.

• Managing end-to-end data engineering processes including data ingestion, transformation, cleansing, validation, and integration from multiple source systems with strong data quality and governance standards.

• Supporting the complete data engineering lifecycle including development, testing, deployment, monitoring, troubleshooting, and performance optimization of production pipelines and platforms.

• Managing analytics-ready data across bronze, silver, and gold layers, supporting multiple consumption patterns including Power BI semantic models, Dataverse, direct BI integrations, and data exports with performance tuning and governance.

• Monitoring and optimizing Snowflake and Azure platform performance, storage, compute utilization, and query efficiency.

• Collaborating with business & leadership teams and stakeholders to translate requirements into scalable technical solutions and supporting automation initiatives using Power Platform, APIs, and Azure services.

• Contributing to DevOps, CI/CD, incident resolution, root cause analysis, and maintaining technical documentation for data architecture, pipelines, and operational processes.

Qualifications for the role:

• Bachelor's or master's degree in computer science, Information Technology, Data Analytics, Engineering, or a related field with minimum 5+ years of hands-on experience in Data Engineering, Data Warehousing, or Analytics Engineering roles.

• Strong expertise in SQL, advanced data transformation, and hands-on experience building scalable ETL/ELT pipelines using Snowflake (Snowpipe, Snowpark, Streams, Tasks, Copy Commands, Stored Procedures) and Microsoft Azure services (Azure Data Factory, Azure SQL, Azure Data Lake, Synapse Analytics, Azure Functions).

• Strong understanding of data warehousing concepts, dimensional modeling, star schema, data lake architecture, and large-scale dataset handling with performance optimization and data quality management.

• Experience supporting analytics and reporting solutions using Microsoft Power BI, Power Platform, and developing analytics-ready datasets.

• Knowledge of Python, APIs, data integration frameworks, middleware, and automation concepts (Mulesoft, etc.) is preferred; DevOps, CI/CD, and Git-based version control are an advantage.

• Strong communication and collaboration skills across technical and business teams, with the ability to work independently, manage priorities, and deliver high-quality solutions under pressure.

• Strong willingness to learn emerging technologies and continuously improve technical expertise.