Participate in Agile/Scrum teams using Azure DevOps and Git for development, deployment, and collaboration.
Document data solutions and stay current with Microsoft Fabric, Azure, and modern data engineering technologies.
MINIMUM AND/OR PREFERRED QUALIFICATIONS
EDUCATION
A bachelor's degree in information technology, Computer Science or or a related technical field.
EXPERIENCE
5+ years of hands-on experience in Data Engineering.
5+ years of experience designing and implementing solutions within the Microsoft Azure Data Platform.
Strong experience with Microsoft Fabric, including Lakehouse, Warehouse, Data Factory, Data Engineering, and Real-Time Analytics.
Strong experience with Azure SQL Database, SQL Server, T-SQL, Synapse Analytics, and Fabric data solutions.
Advanced proficiency in Python, PySpark, and SQL.
Experience designing and implementing modern data warehouses, data marts, and enterprise-scale data platforms.
Experience building and orchestrating ETL/ELT pipelines for analytical and operational workloads.
Experience with data modeling techniques including dimensional modeling and star schema design.
Experience with Git, Azure DevOps, CI/CD pipelines, and Infrastructure as Code concepts.
Strong understanding of cloud security, governance, and data management best practices.
Experience working within Agile/Scrum teams.
Demonstrated ability to lead technical discussions and mentor junior team members.
CERTIFICATES, LICENSES, REGISTRATIONS
Microsoft Certified: Fabric Data Engineer Associate (DP-700).
Microsoft Certified: Fabric Analytics Engineer Associate (DP-600) (preferred).
Databricks Certified Data Engineer Associate (Nice to Have).
Databricks Certified Data Engineer Professional (Nice to Have).
SKILL REQUIREMENTS
Problem-Solving: Advanced problem-solving and analytical skills.
Communication: Strong communication skills to convey technical concepts to non-technical stakeholders.
Collaboration: Effective collaboration with cross-functional teams.
Technical Consulting: Ability to provide technical expertise, recommend solution approaches, and guide implementation decisions to achieve successful project outcomes.
Proactivity: Proactively identifies technical risks, data quality issues, performance bottlenecks, and opportunities for improvement, driving resolutions with minimal supervision.
Adaptability: Passion for continuous learning and adopting emerging cloud, data, analytics, and AI technologies.
Data Architecture: Expertise in data architecture and design principles.
Database Knowledge: Advanced knowledge of relational and non-relational databases.
Data Modeling: Strong understanding of dimensional modeling, data warehousing concepts, and analytics-focused design patterns.
Programming Mastery: Proficiency in in SQL, Python, and PySpark.
Optimization: Experience optimizing Spark workloads, SQL performance, storage architectures, and large-scale data pipelines.
