Cloud Data Engineer

Job Purpose

The Cloud Data Engineer is responsible for designing, building, and optimizing data solutions using modern cloud-based data platforms, with a strong emphasis on Microsoft Fabric, Power BI, and scalable data services. The role involves working with transactional systems hosted in Azure, AWS, and private cloud environments, ensuring efficient data integration, transformation, and analysis to support business intelligence and reporting needs.

Key Responsibilities

• Design & Implement Data Solutions: Develop and manage data pipelines using modern cloud-based technologies, with a strong focus on Microsoft Fabric, integrating data from SQL Server, Oracle, Elastic Search, MongoDB, and other database platforms.

• ETL & Data Processing: Build scalable ETL processes to extract, transform, and load data from various sources, refining it into structured models that support reporting, dashboards.

• Data Modeling & Optimization: Create efficient data models that ensure high-performance analytics, following best practices such as star schema and dimensional modeling.

• Data Governance & Security: Implement security best practices, including role-based access control (RBAC), data classification, and encryption to comply with corporate and regulatory security standards.

• Performance Tuning: Optimize queries, ETL workflows, and BI solutions to ensure scalability, efficiency, and performance. • Business Intelligence & Data Visualization: Design and develop interactive datasets, reports, and dashboards, leveraging Power BI and other BI tools to deliver actionable insights to key stakeholders.

• Collaboration & Stakeholder Engagement: Work closely with technical support, sales, renewals, finance and other teams to ensure data solutions align with business objectives and provide insight in to the SaaS solution developed and delivered by Tungsten Automation.

While the job description describes what is anticipated as the requirements of the position, the job requirements are subject to change based upon any changing needs and requirements of the business.

Qualifications & Required Knowledge

• Expertise in Modern Cloud Data Platforms: Strong knowledge of Microsoft Fabric, including OneLake, Dataflows Gen2, Lakehouses, and Data Warehouses, with experience integrating data from various cloud and on-premises sources.

• ETL & Data Engineering Experience: Hands-on experience with modern ETL tools such as Azure Data Factory, Apache Airflow, AWS Glue, or equivalent pipeline orchestration tools.

• Scripting & Data Extraction: Practical experience using Python, SQL, or similar technologies to extract and manipulate data from various structured and unstructured data sources.

• Data Modeling: Experience designing and implementing star schema, dimensional modeling, and tabular models for efficient analytics and reporting.

• SQL & Query Optimization: Strong proficiency in T-SQL, DAX, and Power Query (M Language) for high-performance data querying and transformation.

• Experience with Cloud Data Services: Familiarity with cloud-native data solutions such as Azure Synapse, AWS Redshift, Google BigQuery, Snowflake, or Databricks.

• Security & Compliance Awareness: Knowledge of best practices in data security, compliance, and governance frameworks to ensure data integrity. Working experience with Microsoft Purview is a plus.

• Performance Optimization: Demonstrated ability to optimize BI solutions, data models, and query performance to enhance efficiency and scalability.