Senior Data Engineer

About The Role

We are seeking a skilled Data Engineer with strong experience in Databricks, Microsoft Fabric, or Snowflake , along with Power BI expertise , to design, build, and optimize scalable data pipelines and analytics solutions. You will also play a key role in enabling effective data visualization and reporting for business stakeholders.

Key Responsibilities

• Design, develop, and maintain ETL/ELT pipelines using Databricks, Fabric, or Snowflake.
• Build and optimize data workflows for performance, scalability, and cost efficiency in cloud environments (Azure/AWS/GCP).
• Implement and manage data lakes, data warehouses, or lakehouse architectures .
• Develop and maintain Power BI dashboards and reports to support business insights and decision-making.
• Collaborate with cross-functional teams to define data requirements, governance standards, and best practices .
• Ensure data quality, integrity, and security across all platforms.
• Automate workflows and support CI/CD deployments for data solutions.
• Monitor and troubleshoot pipelines and dashboards to ensure high availability and reliability.

Required Qualifications

• Bachelors/Masters degree in Computer Science, Information Technology, Engineering, or related field.
• 5-7 years of experience in Data Engineering.
• Proven experience as a Data Engineer with expertise in Databricks OR Fabric OR Snowflake .
• Hands-on experience with Power BI (data modeling, DAX, dashboard creation, performance optimization).
• Strong proficiency in SQL and at least one programming language (Python/Scala/Java).
• Experience with data modeling and building scalable pipelines in cloud environments.
• Knowledge of Azure, AWS, or GCP and their data ecosystem.
• Strong problem-solving, analytical, and communication skills.

Preferred Qualifications

• Experience with streaming data technologies (Kafka, Event Hubs, Kinesis).
• Knowledge of Delta Lake, Synapse, or Snowflake performance optimization .
• Familiarity with DevOps practices for data (CI/CD, Git, Infrastructure as Code).
• Exposure to machine learning pipelines or advanced analytics.
• Understanding of data governance, lineage, and compliance frameworks