Data Engineer

• Own day-to-day administration, configuration, and health of Oura's global Databricks environment, serving stakeholders across IT, business, and data science domains.
• Manage workspace governance: access controls, cluster policies, cost monitoring, job scheduling, and security configurations.
• Support onboarding of new teams and use cases onto the Databricks platform as Oura unifies data across Finance, SDM, Commerce, Marketing, and other domains.
• Proactively identify and resolve performance bottlenecks, reliability issues, and cost inefficiencies.
• Maintain SLAs for platform availability and incident response across global time zones.
• Partner with IT and Data Architecture teams to align platform configurations with Databricks best practices and Oura's Data Mesh approach.

Senior Data Engineer, Data Platform Admin

• Design, build, and maintain saleable data pipelines within Snowflake from multiple sources
• Build and maintain medallion architecture (bronze/silver/gold layers) for data lake solutions
• Design data models that support both businesses intelligence and AI requirements
• Lead cloud-native data platform evolution, integrating Snowflake with AWS.
• Collaborate with business analysts, data scientists, and stakeholders to understand data requirements and translate them into technical specifications.
• Implement data quality frameworks and monitoring systems to ensure accuracy, completeness, and consistency of data assets.
• Develop and maintain comprehensive documentation for data pipelines, models, and processes.
• Optimize query performance and resource utilization to reduce costs and improve processing times.
• Participate in on-call rotation to support production data systems and resolve incidents.

• Leads enterprise data and implementation efforts to integrate customer, client, and transaction data into core reports across all business lines
• Maps and translates raw data into structured formats that enhance reporting and user tool functionality
• Drives enterprise data architecture, including the design and expansion of the Snowflake data model, to support scalable and standardized processor integrations
• Establishes technology-driven oversight processes and leads processor launches, migrations, and system integrations to ensure end-to-end data integrity, compliance, and audit readiness
• Manages teams responsible for reviewing, mapping, and analyzing data provided to the Bank from third parties
• Leads cross-functional implementation efforts across IT, Fraud, FCRM, Compliance, Audit, Operations, and processor partners to ensure successful data onboarding and system integration
• Establishes raw data requirements necessary to operate the bank’s programs compliantly and efficiently
• Interprets compliance regulations for systems and processes to ensure that fraud, risk, operational and compliance controls are in place, reports can be generated, and systems are in accordance with federal, state, and local rules and regulations for the products and industry
• Translates regulatory, fraud, AML, and operational requirements into enterprise data model specifications and scalable monitoring infrastructure
• Provides guidance to map and document data, i.e., new, and existing transaction codes, so system generated data can be categorized for use in research and reporting
• Reviews, audits, and revises the functional specifications documents related to field mapping
• Ensures that data is accurately mapped to processor fields, identify and correct errors as needed
• Disseminate the information to the appropriate teams
• Writes and tests procedures for new systems, systems elevations, and system changes
• Drafts rules, queries, and related process changes driven by new or existing automation and applications
• Communicates new procedures to affected internal parties
• Owns enterprise data governance documentation and implementation standards for new processors, platforms, and system changes
• Leads data and datasets used during system builds, enhancements, modifications, and mapping
• Develops business requirements and works closely with stakeholders to justify project proposals
• Owns data architecture strategy for new processor implementations and major platform migrations, including reconciliation validation, dual-processor transitions, and cutoff population documentation for audit defensibility
• Tests system and process changes to ensure accurate and efficient data entry, maintenance, and reporting
• Ensures workflow quality through structured testing and feedback
• Implements process improvements and enhancements for service delivery
• Reviews, audits, and revises functional specification documents related to the field mapping within the Actimize or other system(s)
• Ensures data is mapped to the processor fields, identify and correct errors
• Leads integration of enterprise data model requirements into Actimize modernization initiatives (e.g., Actimize 2.0), ensuring monitoring scalability and reduction of processor-specific rework
• Creates business unit documentation, including a program parameterization tool mapping processor program IDs to parameters used to identify spend violations and forecast program activity
• Maintains authoritative program parameter and processor mapping frameworks supporting fraud detection, AML monitoring, compliance reporting, and operational forecasting
• Analyzes and integrates multi-technology data systems through a unified back-end platform and web-enabled front-end while owning data quality across data flows
• Designs and expands enterprise-scale data infrastructure (Snowflake and associated systems), enabling standardized ingestion patterns and accelerating future processor onboarding
• Defines common analytics frameworks and tools leveraged across the organization to drive innovation and experimentation
• Establishes enterprise data standards to reduce duplication, eliminate fragmented ETL processes, and improve reporting consistency across business lines
• Transforms data insights into actionable reports, targeting algorithms, and model features and conceptualize coding, deploying, and iterating on predictive analytic solution designs from prototypes to production systems
• Identifies practical approach to test and learns key hypotheses to extend analytic insight
• Establishes criteria for and executes on the creation of alerts and reports for business units with emphasis on Compliance and Fraud/ AML Monitoring teams
• Ensures enterprise-level data integrity supporting audit, regulatory examinations, CIP/KYC integrations, and cross-processor reconciliations
• Performs other duties as assigned.

VP Lead Data Engineer

• Design, build, and maintain scalable Snowflake-based data warehouse architectures, ensuring performance, reliability, and extensibility for future growth.
• Own data architecture and technical design decisions, including data models, integration patterns, and optimization strategies.
• Transform raw source data from multiple internal and external systems into analytics-ready data models using industry best practices.
• Lead and contribute to warehouse development using dimensional modeling techniques (e.g., star schemas, slowly changing dimensions).
• Develop and manage data pipelines using Snowflake features such as Snowpipe, tasks, stored procedures, and performance optimization techniques.
• Manage and monitor data integrations with internal systems via APIs, application calls, or custom code to ensure data accuracy and reliability.
• Apply auditing, change tracking, error handling, and monitoring across large datasets to maintain high data quality standards.
• Identify and resolve data quality issues and implement controls to distinguish valid vs. invalid data used in decision-making.
• Leverage Python and application knowledge to interact with and extract data from other systems.
• Provide technical leadership through coaching and mentoring junior and mid-level data engineers, including code reviews, design feedback, and best-practice guidance.
• Contribute to and help define data engineering standards, best practices, and documentation across the team.
• Collaborate across teams to support day-to-day operational data needs and continuously improve data pipelines and processes.

Senior Data Engineer

• Own End-to-End Delivery of Core Data Platform Components
• Design and ship the data normalization, schema mapping, validation, enrichment, and distribution pipeline for a net-new intelligent data warehouse
• Write production code as a hands-on individual contributor. This is not a role that delegates implementation to others
• Take technical ownership from architecture through deployment, with accountability for reliability, performance, and correctness
• Drive Technical Architecture
• Partner with a small seed team of senior engineers to define the end-to-end architecture for an AI-native data warehouse serving institutional financial clients
• Bring opinionated decisions on schema design, normalization strategies, API exposure patterns, and data distribution approaches
• Evaluate and select technologies with a bias toward what ships well and scales sustainably
• Build AI Evaluation Infrastructure
• Design and implement the evaluation framework that makes AI-generated outputs trustworthy in high-stakes financial data contexts
• Build cross-model comparison tooling, deterministic validation checks, and human-in-the-loop review workflows
• Contribute to shared AI infrastructure that will serve as a foundation for multiple products across the company
• Ship with AI-Native Development Practices
• Use agentic coding tools and LLM-assisted development as your primary workflow; this is how the entire team operates
• Bring strong opinions about how to get the most out of AI-assisted development while maintaining quality and reliability
• Contribute to the team’s evolving practices around AI-accelerated SDLC
• Establish Technical Standards
• As an early senior voice on a new team, set coding standards, review practices, and architectural documentation that will scale
• Help define what “good” looks like for a team building at speed without sacrificing quality
• Mentor and provide technical guidance as the team grows from seed squad to full delivery organization
• Collaborate Across Engineering and Product
• Work closely with your engineering manager, peer engineers, and product management to translate requirements into executable technical plans
• Participate actively in design reviews and roadmap discussions with grounded, implementation-level perspective
• Engage directly with the nuances of private markets data – fund administration, investment data schemas, institutional reporting workflows – to build domain intuition that improves your technical decisions

Staff Software Engineer, Data Platform

• Design the vision and blueprint of an organization’s data framework.
• Design and manage data systems, set policies for how data is stored and accessed, coordinate various data sources within an organization, and integrate new data technologies into existing IT infrastructures.
• Data modeling and design, including software development and database administration.

Data Architect – Data Ecosystems

• Create scalable data processing architectures and solutions
• Ingest data from internal and external sources using cloud-native platforms
• Develop tools and solutions using artificial intelligence, machine learning, and big data techniques
• Help shape departmental strategy and make decisions regarding technical approaches to complex project components

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