Senior Cloud Data Engineer

Mainz Brady Group; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Mainz Brady Group seeks an experienced Cloud Data Engineer to support Investment Operations & Fund Treasury. The role emphasizes hands-on design of Snowflake, Azure, SQL and dbt data solutions for scalable, secure platforms.

You'll design data warehouses, build modular SQL models across staging, intermediate, and mart layers, and create robust pipelines from diverse sources. RBAC, data masking, and CI/CD for transformation code are part of the role in an Agile environment.

Qualifications

• 10+ years of data engineering experience with hands-on delivery.
• Proven experience designing scalable cloud data warehouses.
• Deep Snowflake experience including data modeling, performance tuning, and cost-aware design.
• Advanced SQL skills with strong data warehousing concepts (dimensional modeling, incremental processing).
• Experience with Azure data services (ADF, ADLS) and dbt.

Responsibilities

• Design and implement scalable cloud data warehouse architectures and models.
• Architect physical and logical data models with dimensional modeling where appropriate.
• Build modular SQL models across staging, intermediate, and mart layers with lineage and documentation.
• Create and maintain scalable data pipelines ingesting from diverse sources (Snowflake shares, databases, APIs, event streams, flat files).
• Implement batch and near-real-time ingestion patterns using cloud-native technologies and CDC.
• Optimize warehouse performance with materialization, clustering, and query tuning.
• Establish and maintain RBAC and data security policies (masking, row-level access).
• Develop CI/CD pipelines for warehouse deployments and automated testing.
• Collaborate in Agile sprints using JIRA to deliver high-quality data solutions.

Skills

Cloud data engineering

Data modeling

SQL proficiency

Big data pipelines

Agile development

Education

Bachelor's degree in Computer Science, Information Systems, or related field

Tools

Snowflake

Azure

dbt

Azure Data Factory

Azure Data Lake Storage

Job description

Cloud Data Engineer

Position Summary

We are seeking an experienced, highly skilled Cloud Data Engineer to support an Investment Operations & Fund Treasury Data Engineering team. The ideal candidate will have a strong background in cloud data engineering and architecture, preferably within asset management or financial services, and will be comfortable both assessing architectural approaches and remaining hands‑on in the development and delivery of solutions across modern data technology platforms. This role requires deep expertise in Snowflake, Azure, SQL, dbt, data warehousing, and cloud-native data pipelines, along with the ability to design scalable, reliable, and secure data solutions.

Key Responsibilities

• Design and implement scalable cloud data warehouse architectures, including layer structures, schema design patterns, and partitioning/clustering strategies.
• Architect physical and logical data models that balance query performance, storage efficiency, and business domain clarity, applying dimensional modeling techniques where appropriate.
• Utilize modern cloud data frameworks to build modular, reusable SQL models across staging, intermediate, and mart layers with comprehensive documentation and lineage.
• Build and maintain scalable data pipelines that ingest data from diverse sources, including Snowflake shares, databases, APIs, event streams, and flat files.
• Implement batch and near-real-time ingestion patterns using cloud-native technologies, including incremental loads, CDC (Change Data Capture), and idempotent pipeline design.
• Optimize warehouse and query performance through materialization strategies, clustering keys, and platform-specific query tuning.
• Implement and maintain RBAC, column-level security, dynamic data masking, and row-level access policies to support least-privilege access and data privacy requirements.
• Establish and maintain CI/CD pipelines for warehouse deployments, including automated testing and promotion of transformation code across development, UAT, and production environments.
• Work within an Agile environment using JIRA, participating in sprint planning and delivering high-quality solutions on a consistent cadence.
• Apply AI-assisted development tools where appropriate to accelerate transformation development, data quality automation, and documentation workflows.
• Develop and maintain technical documentation, data models, pipeline runbooks, and data dictionaries.
• Support platform reliability through monitoring, troubleshooting, backfill/reprocessing strategies, operational alerting, and performance optimization.

Qualifications

• 10+ years of data engineering experience, with a demonstrated track record of hands‑on development and end-to-end solution delivery.
• Proven experience designing scalable cloud data warehouse architectures, including layered architectures, schema design patterns, and physical data models.
• Deep hands‑on expertise with Snowflake, including data modeling, performance tuning, cost-efficient design, and secure vendor data shares.
• Advanced SQL skills with strong knowledge of data warehousing concepts, including dimensional modeling, incremental processing, slowly changing dimensions, and semantic layers.
• Strong hands‑on experience building cloud data solutions on Microsoft Azure, particularly Azure Data Factory (ADF) and Azure Data Lake Storage (ADLS).
• Hands‑on experience with dbt, including modular model development across layered warehouse architectures.
• Familiarity with streaming and near-real-time ingestion technologies such as Azure Event Hubs and Kafka, including CDC and incremental processing patterns.
• Strong understanding of data platform reliability, including orchestration, backfill/reprocessing strategies, performance optimization, and operational support.
• Experience developing and maintaining data quality frameworks, operational alerting, and runbooks in SLA-driven environments.
• Experience implementing CI/CD pipelines for dbt and Snowflake, including Git-based workflows, automated testing, and environment promotion.
• Strong written and verbal communication skills with the ability to clearly document data models, pipelines, technical processes, and data definitions.
• Bachelor's degree in Computer Science, Information Systems, or a related field.
• Experience within asset management, investment management, financial services, Investment Operations, or Fund Treasury is strongly preferred.