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JOB SUMMARY:
A Senior Data Engineer at Argano leads the implementation of modern data engineering solutions using Microsoft Fabric and the Azure data platform. Working closely with Data Architects, they provide technical expertise and help translate business and technical requirements into scalable, secure, and reliable data solutions. This role is responsible for building and maintaining data pipelines, integration processes, and analytics-ready data platforms while ensuring data quality, performance, and governance. The Senior Data Engineer collaborates with Data Architects, Software Developers, Data Analysts, and business stakeholders to deliver successful data initiatives and ensure consistent, reliable data delivery across client engagements while continuously improving Argano 's data engineering capabilities and best practices.
RESPONSIBILITIES:
Develop, deploy, and maintain data pipelines and workflows for data ingestion, transformation, and integration from multiple sources.
Build and optimize Lakehouse, Warehouse, and analytics solutions within Microsoft Fabric.
Develop data transformation processes using SQL, Python, and PySpark to support reporting, analytics, and AI initiatives.
Ensure data quality, performance, security, and governance following Microsoft and industry best practices.
Collaborate with architects, developers, analysts, and business stakeholders to deliver data-driven solutions.
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.
