Senior Data Engineer

Primary Responsibilities: Design and implement scalablecloud 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, includingSnowflake 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 maintainRBAC, column-level security, dynamic data masking, and row-level access policiesto support least-privilege access and data privacy requirements. Establish and maintainCI/CD pipelinesfor data warehouse deployments, including automated testing and promotion of transformation code across development, UAT, and production environments. Operate within an Agile environment usingJira, 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 experiencewith 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 withSnowflake, including data modeling, performance tuning, cost optimization, and secure vendor data shares. AdvancedSQLskills 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 inMicrosoft Azure, particularlyAzure Data Factory (ADF) and Azure Data Lake Storage (ADLS). Strong proficiency withdbt, including modular model development across layered warehouse architectures. Experience with streaming and near-real-time ingestion technologies such asAzure 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 maintainingdata quality frameworks, operational alerting, and runbooks to support SLA-driven environments. Experience implementingCI/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 withinasset management, financial services, investment management, fund accounting, or investment operations. Experience working with complex financial or investment data in highly governed environments.