Azure Cloud Engineer

Nelson Connects; Santa Rosa (CA); On-site; USD 110,000 - 160,000; Full time

Job summary

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

You will collaborate with cross-functional teams, apply strong technical expertise, and mentor teams on modern data engineering practices while ensuring

Qualifications

• Experience designing scalable data architectures on cloud platforms.
• Strong SQL, Python, PySpark with data modeling.
• Knowledge of data governance and security compliance for financial data.

Responsibilities

• Design, implement, and manage scalable data architectures and pipelines on cloud platforms.
• Create automated ETL/ELT workflows for ingestion, cleansing, and transformation.
• Load transformed data into cloud data warehouses and reporting systems.
• Tune performance of large data pipelines, queries, and architectures for reliability and cost-efficiency.

Skills

Python

SQL

PySpark

Tools

Azure Data Factory

Databricks

Azure Data Lake Storage

Azure Synapse

Analysis Services

Job description

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

• 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).
• 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