Senior Data Engineer

Quantiphi

. Design, build, and maintain cloud-native data pipelines and data lake architectures in Snowflake for Asset-Backed Finance investment and portfolio analytics .

Posted 10/16/2026full-timeRemote • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and maintaining cloud-native data pipelines and architectures, particularly in Snowflake, while ensuring data integrity and supporting analytics use cases. Proficient in managing ETL/ELT workflows and utilizing AWS services for effective data platform development.

Highest-signal resume keywords

Snowflake ArchitectureETL/ELT Workflow ManagementPython ProgrammingAWS Services ProficiencyDagster Workflow Orchestration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringSQL Performance TuningData ModelingData TransformationData MappingFinancial Data Integrity ChecksAgile MethodologiesData Lake ArchitectureComplex Data TransformationEnterprise Analytics

Soft Skills

Cross-Team CoordinationProject PlanningCommunication

Tools & Technologies

JiraDagsterAWS S3AWS GlueAWS LambdaAWS IAMAWS ECSAWS EMRAWS Redshift

Industry Keywords

Asset-Backed FinanceInvestment ManagementPortfolio AnalyticsEnterprise Data ModelsData Warehouse Architectures

Tech Stack

Tools & technologies

Amazon RedshiftAWSCloudETLPythonSQL

About the role

Key responsibilities & impact

Design, build, and maintain cloud-native data pipelines and data lake architectures in Snowflake for Asset-Backed Finance investment and portfolio analytics
Develop and manage scalable ETL/ELT workflows to ingest, transform, and curate structured and unstructured data
Support analytics and reporting use cases, including data reconciliation, validation, and financial data integrity checks
Support migration from legacy systems to a modern data platform through data mapping, transformation logic, and architecture alignment
Break down work into Epics/User Stories in Jira
Support project planning, timelines, and cross-team coordination
Assist with testing, cutover planning, and go-live readiness

Requirements

What you’ll need

8+ years of progressive experience in data engineering, data platform development, or enterprise analytics
Proven track record building enterprise data models and warehouse architectures within financial services, investment management, or ABF/portfolio analytics domain contexts
Advanced proficiency in Snowflake, including SQL performance tuning, architecture, security, and schema design
Hands-on expertise using Dagster for workflow orchestration, dependency management, and monitoring
Deep familiarity with core AWS services, including S3, Glue, Lambda, IAM, ECS, EMR, or Redshift
Expert-level mastery of Python and advanced SQL for complex data transformation and ELT design
Proficiency with Jira for backlog management and Agile sprint planning

Benefits

Comp & perks

Exposure to working with Fortune 500 companies and innovative market disruptors
Exposure to the latest technologies related to artificial intelligence and machine learning, data and cloud
Energetic team of highly dynamic and talented individuals
Opportunity to be part of an AI-first digital transformation and engineering company