Senior Data , BI & AI Enablement Engineer

Description: The data engineer is responsible for designing, developing, and maintaining the infrastructure and systems required for data storage, processing, and analysis. They play a crucial role in building and managing the data pipelines that enable efficient and reliable data integration, transformation, and delivery for all data users across the enterprise.

Responsibilities: · Designs, develops, and maintains robust data pipelines using Databricks and Apache Spark, that extract data from various sources, transform it into the desired format, and load it into the appropriate data storage systems. · Implement and manage advanced data products in our medallion architecture. · Implement and manage advanced data models, including dimensional, relational, and Data Vault modeling techniques. · Integrates data from different sources, including databases, data warehouses, APIs, and external systems. · Ensures data consistency and integrity during the integration process, performing data validation and cleaning as needed. · Transforms raw data into a usable format by applying data cleansing, aggregation, filtering, and enrichment techniques. · Optimizes data pipelines and data processing workflows for performance, scalability, and efficiency. · Monitors and tunes data systems, identifies and resolves performance bottlenecks, and implements caching and indexing strategies to enhance query performance · Implements data quality checks and validations within data pipelines to ensure the accuracy, consistency, and completeness of data. · Manage Databricks Unity Catalog including administrative activities (policies, security, access control, cost control). · Optimize and administer Databricks environments to ensure high performance and reliability. · Cluster configuration and policy management in Databricks. · Build and maintain CI/CD pipelines (DevOps) in GitHub or Azure DevOps. · Collaborate with cross-functional teams to deliver comprehensive data solutions. · Ensure data quality, security, and governance across all data processes. · Communicate effectively with stakeholders to understand requirements and deliver actionable insights. · Operate within agile teams, contributing to continuous improvement in data engineering practices and processes. · Utilize cloud platforms such as AWS and Azure to build scalable data solutions. · Collaborates with data scientists and data analysts to optimize models and algorithms for data quality, security, and governance.