Senior Data Engineer

Mainz Brady Group; San Francisco (CA); On-site; USD 150,000 - 230,000; Full time

Job summary

Mainz Brady Group is seeking a Senior Cloud Data Engineer in San Francisco to design and implement scalable cloud data warehouse architectures. The role emphasizes hands-on data modeling, modern data pipelines, and secure data access across Snowflake and Azure platforms.

You will work on end-to-end data solutions for investment operations, leveraging dbt, SQL, and streaming technologies. Strong collaboration in an Agile environment and a focus on data quality are essential.

Qualifications

• 10+ years of data engineering experience with hands-on development and end-to-end delivery.

Responsibilities

• Design and implement scalable cloud data warehouse architectures, including layered structures and partitioning/clustering strategies.
• Architect physical and logical data models balancing query performance and storage; apply dimensional modeling.
• Build modular, reusable SQL models across staging, intermediate, and mart layers with documentation and lineage.
• Develop scalable data pipelines ingesting from Snowflake shares, databases, APIs, event streams, and flat files.
• Implement batch and near-real-time ingestion patterns using cloud-native tech, including CDC and idempotent design.
• Optimize Snowflake performance with materialization, clustering, and cost-efficient practices.
• Establish and maintain RBAC, column-level security, dynamic data masking, and row-level access policies.
• Set up CI/CD pipelines for data warehouse deployments including testing and promotion across environments.
• Operate in Agile with Jira, delivering high-quality solutions on a regular cadence.
• Utilize AI-assisted tooling to accelerate transformation development, data quality automation, and documentation.

Skills

Snowflake

Azure Data Factory

Azure Data Lake Storage

dbt

SQL

Data Modeling

CDC / Change Data Capture

Data Warehousing

CI/CD

Jira

ETL / ELT

Education

Bachelor's degree in Computer Science / Information Systems or related

Tools

Snowflake

Azure Event Hubs

Kafka

Job description

We are seeking an experienced, highly skilled Senior Cloud Data Engineer to support an Investment Operations and Fund Treasury Data Engineering team. The ideal candidate will bring strong experience in asset management or financial services, be comfortable evaluating architectural approaches, and remain highly hands-on in designing and implementing solutions across modern data technology platforms.

Primary Responsibilities

• Design and implement scalable cloud data warehouse architectures, including layered 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 data transformation 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 information 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 Snowflake and warehouse performance through materialization strategies, clustering keys, query tuning, and cost-efficient design.
• 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 data warehouse deployments, including automated testing and promotion of transformation code across development, UAT, and production environments.
• Operate within an Agile environment using Jira, participating in sprint planning and delivering high-quality solutions on a consistent cadence.
• Leverage AI-assisted development tools where appropriate to accelerate transformation development, data quality automation, and documentation.

Qualifications

• 10+ years of data engineering experience with a strong 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, and physical data modeling.
• Deep expertise with Snowflake, including data modeling, performance tuning, cost optimization, and secure vendor data shares.
• Advanced SQL skills and strong knowledge of data warehousing concepts, including dimensional modeling, incremental processing, slowly changing dimensions, and semantic layers.
• Hands-on experience designing and operating data solutions in Microsoft Azure, particularly Azure Data Factory (ADF) and Azure Data Lake Storage (ADLS).
• Strong proficiency with dbt, including modular model development across layered warehouse architectures.
• Experience with streaming and near-real-time ingestion technologies such as Azure Event Hubs and Kafka, including CDC and latency-aware pipeline design.
• Strong understanding of data platform reliability, including orchestration, backfills, reprocessing strategies, monitoring, and warehouse performance optimization.
• Experience designing and maintaining data quality frameworks, operational alerting, and runbooks to support SLA-driven environments.
• Experience implementing CI/CD for dbt and Snowflake, including Git-based workflows, automated testing, and environment promotion.
• Strong written and verbal communication skills with experience producing data models, pipeline documentation, runbooks, and data dictionaries.
• Bachelor's degree in Computer Science, Information Systems, or a related discipline.

Preferred Experience

• Experience within asset management, financial services, investment management, fund accounting, or investment operations.
• Experience working with complex financial or investment data in highly governed environments.