Cloud Data Engineering Manager

POSITION SCOPE AND ORGANIZATIONAL IMPACT

Moss' Cloud Data Engineering Manager is responsible for any technological duties associated with cloud computing, including design, planning, management, maintenance, and support. This is a hands‑on role that requires the manager to perform tasks, implementation, and Data Engineering tasks, as well as DBA tasks when needed.

• Communicates with Stakeholders.

• Accepts tasks from management and the client.

• Design, build, and maintain scalable and secure cloud-based data solutions primarily within the Microsoft Azure ecosystem.

• Lead the development of data pipelines and ETL/ELT workflows using Azure Data Factory, ensuring efficiency, reliability, and reusability across projects.

• Develop and optimize data lake and data warehouse architectures using Azure Data Lake Storage Gen2, Azure Synapse Analytics, and SQL Server to support advanced analytics and business intelligence initiatives.

• Implement robust data ingestion frameworks for both structured and unstructured data sources, including APIs, on‑premises databases, IoT, and 3rd party systems like Workday and Procore.

• Design and enforce data governance standards, metadata management, and data security policies, including row-level security and data masking using Azure Purview and SQL Server security roles.

• Build real-time and batch data processing solutions leveraging Azure Stream Analytics, Event Hubs, and Databricks, where applicable.

• Collaborate with data scientists, analysts, and business stakeholders to design and deliver semantic models, Power BI datasets, and dashboards that translate raw data into actionable insights.

• Manage CI/CD processes for data infrastructure using Azure Dev Ops, ARM templates, and YAML pipelines. Monitor and optimize data workloads using Azure Monitor, Log Analytics, and SQL Profiler, ensuring high availability, performance, and cost-efficiency.

• Provide technical mentorship and guidance to junior engineers, fostering a culture of innovation and continuous learning.

• Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Engineering, or related field.

• 7 years of professional experience in data engineering, with at least 3 years focused on Microsoft cloud data technologies.

• Proven experience in building enterprise‑grade solutions on Azure, including:
  Azure Data Factory for orchestration, Azure Synapse and SQL Server for data warehousing, Azure Data Lake Storage Gen2 for scalable storage, and Azure Functions and Logic Apps for automation.

• Strong expertise in T‑SQL, including writing optimized queries, stored procedures, and views for high‑performance analytics.

• Experience in implementing data modeling best practices (star/snowflake schema), dimensional modeling, and snapshotting techniques.

• Hands‑on experience integrating with Microsoft Power Platform, especially Power BI, including DAX, M language, and dataset optimization.

• Familiarity with Agile/Scrum methodologies, source control via Git, and project management tools like Azure Boards or Jira.

• Experience with data governance and compliance, including data lineage, audit trails, sensitive data classification, and GDPR/CCPA requirements using Azure Purview or similar tools.

• Data Pipeline Development:
  Design, implement, and optimize end‑to‑end data pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data.
  Develop robust ETL (Extract, Transform, Load) processes to integrate data from diverse sources into our data ecosystem.

• Implement data validation and quality checks to ensure accuracy and consistency.

• Work independently and with the business stakeholders to understand their requirements and drive business outcomes.

• Experienced in SQL Server Database Administration, the utilization of SQL Server database for backend development and integration services, and possessing advanced SQL concepts.

• Solid understanding of data concepts, including ODS, data warehousing, and data storage.

• Ability to think strategically about design and architectural aspects of the data infrastructure.

• Proficiency with joins and subqueries.

• Experience using aggregate functions.

• Knowledge of indexing and query optimization techniques.

• Understanding of database normalization principles.

• Familiarity with referential integrity best practices.