Data Engineer

• Participation and representation in a client facing Data Governance Committee, including making recommendations, guiding decisions and recommendations within the scope of Client, State and Federal data governance policies, practices, and standards relevant to data management
• Ensure and maintain to the highest standards the quality and security of data within program engagement through adherence to processes and procedures for access controls and monitoring of data activities and use to protect data integrity, including ensuring compliance for the preservation and protection of Protected Health Information (PHI) and Protected Identification Information (PII)
• Collaborate with stakeholders in the design, testing, implementation and ongoing maintenance and support activities of data services
• Assist the Client in solving data-related issues by managing data corruption or mapping data between program areas
• Maintain internally established data quality reporting metrics, evaluate, and identify issues/corrections and coordinate and implement data management best practices
• Identify data assets, lineage, and business rules within data domains to ensure data element continuity and avoid data conflict
• Manage data design, creation and management of database architecture and data repositories
• Maintain accurate and current content within the data management plan including all diagrams, workflows, and all other data related documentation
• Conduct and comply with data reviews and auditing to ensure adherence to current standards, policies and design of the database architecture and repositories
• Ensure documentation and processes are followed, including Architectural Board approvals and other necessary approvals, for all data architecture changes

Data Architect

• 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

• Serves as a lead in the analysis of business requirements associated with the design and implementation of highly complex, highly scalable enterprise data processes
• Designs and develops robust and efficient processes for extracting, loading and transforming data to meet the needs of enterprise scale projects
• Provides guidance and participates in activities involved in the analysis, design, development, troubleshooting, debugging and maintenance of an enterprise data management and data ecosystem
• Identifies, investigates, tracks, and resolves the most complex technical errors ensuring the solution is timely and of acceptable quality
• Establishes and enforces database standards, guidelines, procedures and quality assurance for database deliverables such as conceptual design, logical data model capacity planning, external data interface specs, data loading, data maintenance plan and security
• Reviews and analyzes data objects and models to improve data management and data access
• Designs, constructs, tests and implements extraction and transformation programs, processes and cycles for historical and current source data
• Creates technical specification for supporting enterprise data transformation processes and supplementing system documentation for ETL processes
• Performs data profiling, source-target mappings, data transformation development, optimization, testing and implementation
• This position participates and serves as a lead in maintaining the ETL environment for the company’s system-wide enterprise data warehouse.

Supply Chain Data Engineer

• Architecting, creating and maintaining data pipelines
• Assist with renovating the data management infrastructure to drive automation in data integration and management
• Work in partnership with data science teams and with business analysts in refining their data requirements
• Train counterparts across the organization in data pipelining and preparation techniques
• Work with data governance teams and participate in vetting and promoting content created in the business
• Demonstrated ability to communicate complex results to technical and non-technical audiences
• Work effectively in teams as well as independently across multiple tasks
• Strategic, intellectually curious thinker with focus on outcomes

Senior 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.

• 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

• Manage and design data architecture to ensure data is accessible, secure and reliable
• Responsible for defining data flow guidelines, ETL pipelines and process
• Responsible for defining efficient SQL query guidelines and layers
• Overseeing database schema and mapping for all product requirements
• Responsible for cloud connectivity to data lakes, warehouses, event hubs and MQTT
• Gather, clean and analyze data to help customers make business decisions
• Design data systems for AI/ML and predictive analytics within I40
• Expected to Travel 15% of the time

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