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

• Develop and optimize data and data pipeline architecture
• Provide thought leadership to support software developers and data scientists
• Assemble large, complex data sets that meet functional requirements
• Identify and implement internal process improvements
• Build AWS infrastructure for data extraction and transformation
• Automate deployment and management of data services using IaC
• Contribute to CI/CD processes
• Develop analytics tools for actionable insights

Senior Data Engineer – Temp to Hire

• Assist in the design, build, testing, deployment, and production support of core data warehouse and analytics solutions
• Perform initial data exploration steps such as profiling, understanding data quality, binning, pivoting, and summarizing
• Consult with data SMEs to understand and address data quality issues
• Support the design, build, test, and deployment of data warehouse solutions
• Provide modeling analysis and develop data models according to specifications and standards
• Support the data cleaning, integration, and transformation of data according to the defined target data model

Data Warehouse Developer

• Collaborate with scientific teams to design scalable data capture systems and user-friendly visualizations of experimental results
• Design and implement database architectures, data models, and automated data pipelines across platforms (e.g., Amazon Web Services, Laboratory Information Management Systems, Benchling, and contract research organization systems)
• Develop structured data models capturing microbial strain metadata, growth conditions, and experimental outputs
• Partner with researchers to standardize experimental metadata, ontologies, and data structures to improve data quality, reproducibility, and traceability
• Build and maintain data warehouses supporting probiotic growth, strain characterization, and clinical datasets
• Develop and implement robust data quality and integrity checks across data ingestion workflows, including integration of externally generated clinical data
• Build pipelines integrating multi-modal datasets (e.g., sequencing, metabolomics, in vitro assays, and clinical endpoints)
• Partner cross-functionally to prototype and deploy digital solutions that enhance research and development efficiency and scalability

Life Science Data Engineer

• Perform Data Warehouse Architecture at an intermediate level
• Design and develop data models and ETL specifications
• Conduct data analysis and recommend solutions
• Participate in data warehouse and dashboard architecture planning
• Propose requirements, standards, and best practices for data modeling and ETL
• Conduct product and vendor research and present recommendations

IS Data Warehouse Architect II

• 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

• Define and maintain the technical vision, architecture principles, and engineering strategy for the Solution Data Repository.
• Lead the design and development of an enterprise ontology to inform the technical architecture.
• Drive practical interoperability between catalog/SDF sources and downstream tools.
• Implement and enforce engineering lifecycle standards, code quality practices, release discipline, and operational readiness criteria.

Enterprise Data Architect – Solutioning Suite

• Design and develop data serving and semantic layers to support business needs
• Develop data models for use by analysts and applications using modern dimensional modeling practices
• Build and maintain robust, scalable data pipelines using ETL, ELT processes
• Create efficient and performant data models to support business requirements
• Implement and automate data quality checks and monitoring to ensure data accuracy
• Manage, troubleshoot and optimize existing data infrastructure, including cloud-based solutions
• Leverage cloud data platforms (Fabric, Databricks, Snowflake, etc.) for data storage, processing, and analysis
• Collaborate with data analysts, data scientists, and business stakeholders to understand their data needs

• Develop scalable, well‑documented ETL/ELT pipelines using T‑SQL, Python, Azure Data Factory/Fabric Data Pipelines, and Databricks; implement best‑practice patterns for performance, security, and cost control.
• Design relational and lakehouse models; create Fabric OneLake shortcuts, medallion‑style layers, and dimensional/semantic models for Power BI.
• Build automated data‑quality checks, lineage, and observability metrics; contribute to CI/CD workflows in Azure DevOps or GitHub.
• Gather requirements, demo iterative deliverables, document technical designs, and translate complex concepts to non‑technical audiences.
• Research new capabilities, share findings in internal communities of practice, and contribute to reusable accelerators.
• Collaborate with clients and internal stakeholders to design and implement scalable data engineering solutions.

• Define and drive the product vision, strategy, and roadmap for the Data Curation Pipeline team
• Lead the modernization of healthcare data pipelines that ingest and transform raw claims, prescription, and EHR data into standardized enterprise datasets
• Identify opportunities to improve data quality, scalability, usability, speed, and downstream product readiness
• Partner with internal stakeholders to understand business needs and translate them into product priorities and roadmap decisions
• Own and manage the team backlog, ensuring clear prioritization of high-impact initiatives, technical enablers, defects, and enhancements
• Serve as the primary product point of contact for engineering, architecture, and cross-functional stakeholders
• Define acceptance criteria and participate in user acceptance testing and release validation
• Work closely with software developers, data engineers, architects, QA, analytics teams, and business stakeholders throughout the product lifecycle
• Lead Agile product processes including backlog refinement, sprint planning input, prioritization, and stakeholder communication
• Build strong relationships with internal business stakeholders, product teams, and data consumers across the organization

Senior Product Manager, Data Platform – IntelliScript

• Manage a team by hiring and onboarding talent, setting clear expectations, conducting formal performance reviews, and coaching accountability.
• Lead the team to deliver on the enterprise data platform roadmap by translating architecture direction into sequenced execution plans, team backlogs, and measurable outcomes.
• Create a strong engineering culture across the team by setting standards for maintainability, organization, and repeatability; growing engineers via regular 1:1s, timely feedback, development plans, and formal review cycles; and being responsible for overall team performance and cohesion.
• Own day-to-day execution for the Data Engineering team by breaking work into tasks, driving sprint-level planning, delegating effectively to grow ownership, unblocking delivery, and ensuring high-quality outcomes through engineering reviews and feedback.
• Drive implementation of the Medallion architecture (Bronze → Silver → Gold) with strong enforcement of layer responsibilities, ensuring repeatable and auditable pipeline behavior leveraging Boomi, Dbt, Azure Data Bricks, Boomi/Rivery, Delta tables, and Sql Server.
• Partner closely with the Data Architect to bring the architecture to life through concrete implementation patterns, reference pipelines, and guardrails—ensuring the team can consistently execute the intended approach.
• Reduce friction by driving crisp decisions and forward motion—using lightweight decision records, seeking the right input, and then executing accountability (while avoiding overly heavy process).
• Establish DataOps practices aligned with modern software development lifecycle (SDLC): CI/CD for data assets, consistent branching/release patterns, code review standards, and runbooks/operational readiness for pipelines.
• Improve pipeline observability and operational reliability by implementing monitoring for freshness/staleness, failure modes, and quality signals, and ensuring the team responds predictably to incidents and support requests.
• Drive stakeholder partnership and intake stream: collaborate with business partners to clarify requirements, reduce ad-hoc thrash, and shape requests into deliverable, well-scoped work that fits the platform strategy.
• Develop future leaders and succession plans for key roles through assigning regularly occurring responsibilities, mentoring, and strategic delegation.
• Support and promote the company values through positive interactions with both internal and external stakeholders on a regular basis.
• Other strategic business initiatives or special cross-functional project involvement as required.

Manager, Data Engineering

• Collaborate with the Data and AI team to design and build data infrastructure, including ETL/ELT pipelines using Azure Data Factory.
• Develop and maintain data models, cloud data warehouses, and data lakes using Azure SQL Server and Azure SQL Database.
• Write and optimize T-SQL scripts, stored procedures, and functions for data transformation and integration.
• Support data quality, governance, and security initiatives.
• Participate in the full lifecycle of data projects, from requirements gathering to deployment and monitoring.

• Build and maintain high-performance pipelines using Airflow, dbt, and Python to organize raw data for analysis, modeling, and product use cases
• Partner with Product, Data Science, and ML Engineering to build the infrastructure supporting machine learning workloads, including training datasets, feature pipelines, and model output tables
• Write production-grade SQL (Amazon Redshift) and manage query optimization to support rapidly growing business needs
• Build instrumentation, monitoring, and alerting to guarantee data freshness, quality, and availability in production environments
• Identify and implement improvements by automating manual tasks and reducing pipeline runtimes
• Utilize AI-assisted developer tools (like Claude Code) daily to accelerate development and participate in design reviews and on-call rotations

• Deploy new data pipelines
• Design & build data observability platforms and metrics
• Build metadata driven pipeline solutions
• Monitor and optimize data transformations
• Build third party data integrations

• Build Pipelines: Design and maintain scalable data pipelines to ingest and enrich healthcare data using Databricks and Spark.
• Optimize Data Workflows: Improve data intake processes and optimize SparkSQL/Python workloads for performance, scalability, and cost efficiency.
• Design Data Models: Partner with senior engineers to develop data marts and semantic models that power analytics products and reporting.
• Ensure Quality: Monitor pipeline health, troubleshoot failures, and implement data validation and quality controls.
• Learn & Grow: Expand your knowledge of the full data lifecycle, cloud infrastructure (Azure/AWS), and healthcare data standards.

Data Engineer – Entry Level

• 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

• Meet with clients to understand their current business processes and needs to provide consulting services and direction on how to build or grow their current data strategy.
• Work with HBS Sales Solutions consultants to identify and grow opportunities within HBS client environments.
• Support and administer the underlining infrastructure and layout of a client data environment.
• Develop and design the process for the customer data collection process.
• Develop policies and procedures for the collection and analysis of data.
• Review customer sources to ensure integrity of the data collection process.
• Collaborate with the BI Data Developers to ensure the requirements are being met to build the right solution needed.
• Estimate development effort required to deliver data customer needs and requests.
• Use business analysis skillset to identify development needs for the purpose of streamlining and improving the operations of the organization for efficiency and profitability.
• Ability to work independently or as a team on project-based solutions for clients.
• Work with team mates to continue to grow and mature data services and delivery options for HBS clients.
• Based on experience, one may mentor other engineers in developing scalable, secure, high-performance BI and Data solutions.
• Meet annual billable hour goal, as defined by HBS. This number may change over time as the business evolves.

• 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

• Acts as a subject matter expert and thought leader within the Data Platform Domain
• Serves as a thought leader in data processing design and implementation, defining advanced structure for moving, storing, and maintaining high-quality data.
• Leads projects by managing timelines, coordinating teams, and communicating project statuses.
• Serves as a subject matter expert on governance standards, continuously aligning data practices with evolving industry best practices and requirements
• Contributes to defining the overall vision and strategy for data engineering within the organization, ensuring alignment with organizational goals and long-term objectives
• Establishes visionary goals, advises on strategic plans, employs advanced monitoring, influences high-level stakeholders, and delivers transformative results
• Expansion of our Data Warehouse(s) and Lakehouse solutions for a healthcare data focused enterprise
• Configuring and maintaining unity catalog to enable enterprise data lineage, data quality, auditability and data stewardship
• Building out Data Security protocols and best practices including the management of identified and de-identified (PHI/PII) solutions
• Always ensure a policy of least privilege is followed for anything being implemented
• Building data solutions for clients while upholding the best standards for reliability, quality, and performance
• Building solutions within Delta Live Tables and automation of transformations
• Building out performant enterprise-level medallion architecture(s)
• Building fit-for-purpose near real-time streaming and batch solutions
• Building out performant and efficient enterprise solutions for internal and external users for both structured and unstructured healthcare data
• Building out Infrastructure as Code using Terraform and Asset Bundles
• Working with the business to build cost effective and cost transparent Data solutions
• Help architect, build, and maintain robust and scalable data pipelines, monitoring, and optimizing performance
• Identify and implement improvements to enhance data processing efficiency
• Design and implement reliable and resilient Event Driven data processing
• Lead design, implementation, and maintenance of standards based (FHIR, OMOP, etc.) and efficient data models for both structured and unstructured data
• Assemble large, complex data sets that meet functional and non-functional business requirements
• Develop and maintain data models, ensuring they align with business objectives and data privacy regulations
• Partner internally and externally with key stakeholders to ensure we are providing meaningful, functional, and valuable data
• Effectively work with Data, Development, Analysts, Data Science, and Business team members to gather requirements, propose, and build solutions.
• Communicate complex technical concepts to non-technical stakeholders and provide guidance on best practices.
• Ensure that technology execution aligns with business strategy and provides efficient, secure solutions and systems
• Gather requirements and build out project plans to implement those requirements with forecasted efforts to implement
• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
• Build analytics tools that utilize the data pipeline to provide actionable insights into operational efficiency and other key business performance metrics.
• Create data tools for clinical, analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader.
• Lead investigation of new tooling, development implementation plans, and deployment of necessary tooling

Lead Data Engineer, Data Platform – IntelliScript

• Own the enterprise data standards
• Establish and chair data design reviews
• Define reference architectures and canonical models
• Author data integration patterns and quality standards
• Set ingestion/promotion standards for analytics platform
• Encode data policy requirements into architecture artifacts
• Maintain architecture and environment documentation

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