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

· Bachelor’s/Master’s degree in Computer Science, Information Technology, Engineering, or related field.

· 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.