Azure Cloud Engineer

Nelson Connects; Santa Rosa (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Nelson Connects is seeking a Cloud Data Engineer in Santa Rosa, CA to design, implement, and manage scalable data solutions primarily on Microsoft Azure and other cloud platforms. You will oversee the end-to-end data lifecycle, including pipeline development, storage architecture, processing, and integration to empower data-driven decision-making across the organization.

You will collaborate with cross-functional teams and stakeholders, optimize pipelines for performance and cost, and mentor IT

Qualifications

• Minimum of 3+ years designing and deploying cloud data solutions on Azure.
• Proficient in Python, SQL, and PySpark for data manipulation and pipelines.
• Experience with Azure Data Factory, Databricks, ADLS, and Synapse Analytics.
• Strong understanding of data warehousing and dimensional modeling, including SCD.
• Familiarity with data security, governance, and regulatory compliance in financial contexts.

Responsibilities

• Design and build scalable data architectures, storage environments, and processing pipelines.
• Develop ETL/ELT workflows and manage data ingestion and transformation.
• Load data into cloud data warehouses and analytics systems.
• Tune performance of pipelines and queries for cost efficiency.
• Monitor data environments and provide on-call support.
• Document architectures and collaborate with IT and business stakeholders.
• Mentor junior staff on cloud data engineering best practices.
• Partner with analysts to enable analytics and insight generation.

Skills

Python

SQL

PySpark

Education

Microsoft Certified: Azure Data Engineer Associate

Tools

Azure Data Factory

Databricks

Azure Data Lake Storage (ADLS)

Azure Synapse Analytics

Analysis Services

Job description

Job Summary

Our client is seeking a skilled Cloud Data Engineer to design, implement, and manage scalable data solutions primarily on Microsoft Azure and other cloud platforms. In this role, you will oversee the end-to-end data lifecycle- including pipeline development, storage architecture, processing, and integration to empower data-driven decision-making across the organization. You will leverage strong technical expertise and analytical problem-solving skills to collaborate closely with cross-functional teams and line-of-business stakeholders.

Key Responsibilities

Data Architecture & Pipeline Engineering

• Design & Build: Develop scalable, secure, and resilient data architectures, storage environments, and processing pipelines using cloud-native services.
• ETL/ELT Workflows: Create automated workflows to streamline data ingestion, cleansing, mapping, transformation, and distribution using slowly changing dimensions (SCD) in a enterprise data warehouse environment.
• Storage & Integration: Load optimized, transformed data into target structures, including cloud data warehouses, reporting systems, and downstream analytics applications.
• Performance Tuning: Optimize 'big data' pipelines, queries, and architectures to ensure high performance, reliability, and cost-efficiency.

Operations, Security & Compliance

• System Health & Support: Monitor, troubleshoot, and maintain data environments to support high availability; provide on-call support for critical production systems as needed.
• Data Security & Governance: Implement robust security controls—including encryption, access management, and auditing to protect sensitive data and support compliance with financial institution regulations.
• Documentation & Planning: Maintain clear documentation for data pipelines, system architectures, and procedures. Collaborate with IT teams to draft technology and security roadmap plans.
• Mentorship & Culture: Mentor IT staff on security best practices, emerging cloud technologies, and continuous learning in modern data engineering.

Cross-Functional Collaboration

• Requirement Gathering: Partner with business analysts, technical teams, and key business stakeholders to translate operational needs into technical data solutions.
• Analytics Enablement: Deliver clean, accessible datasets and leverage analytics services to generate actionable business insights.

Qualifications & Skills

Technical Experience

• Cloud Platform: Proven experience as an Azure Data Engineer or in a similar cloud-focused data role.
• Core Languages: Fluency in Python, SQL, and PySpark for data manipulation and pipeline development.
• Azure Data Ecosystem: Hands-on experience with Azure Data Factory, Databricks, Azure Data Lake Storage (ADLS), Azure Synapse Analytics / SQL DW, and Analysis Services.
• Data Warehousing: Solid background in building ETL/ELT pipelines, big data environments, and modeling dimensional data (e.g., slowly changing dimensions).

Professional & Operational Competencies

• Work Management: Demonstrated ability to work independently with minimal supervision, prioritize tasks using browser-based tracking platforms (JIRA preferred), and thrive in a fast-paced environment.
• Communication & Collaboration: Strong verbal and written communication skills with a track record of building positive relationships with internal teams, leadership, and external vendors.
• Regulatory Awareness: Familiarity with IT laws, rules, and compliance regulations specific to financial institutions.

Preferred Certifications

• Microsoft Certified: Azure Data Engineer Associate (or equivalent cloud data certification) preferred.