Data Engineer

• Own the design, build and operation of the data lake and ingestion platform end-to-end, from architecture through production reliability.
• Build low-latency batch and streaming pipelines that ingest signals from internal and external sources, normalize them to a common schema, enrich them with context and serve model-ready data to the layers above.
• Make adding a new data source a routine task rather than a project, so our view of risk keeps widening over time.
• Establish data quality, freshness, completeness, lineage and observability so the platform is trustworthy enough to automate on top of.
• Build data pipelines that ground generative AI, including unstructured text and threat intelligence processing, embedding generation, vector storage and retrieval.
• Own deployment, CI/CD and operational reliability of the platform on Kubernetes.
• Partner with data science, product and architecture to turn the platform into a shared foundation across 360 Fraud Protection.

Staff / Principal Data Engineer

• Design and implement robust ETL/ELT pipelines
• Build and maintain Feature Stores and specialized datasets
• Develop the data pipelines required for Generative AI
• Act as the technical lead for our Snowflake data warehouse
• Manage complex, cross-platform data workflows using Airflow, Step Functions, or Azure Data Factory
• Partner directly with central IT, Database Administrators, and Security teams
• Implement automated validation and observability layers
• Drive the efficiency of our data stack by optimizing storage
• Work as a dedicated engineering partner to MLOps and Data Science teams

Data Engineer, AI

• Own data architecture end-to-end. Define how we capture, model, and serve critical business data—then implement it in production.
• Build mission-critical pipelines. Develop and operate batch data workflows that process high-volume events related to notifications with tight guarantees for latency, completeness, and accuracy.
• Design and implement canonical models. Create domain-oriented data models that serve as the source of truth for analytics, ML, and production applications.
• Enforce data quality at scale. Build tests, lineage, monitoring, and reconciliation systems that make every dataset observable and every anomaly actionable.
• Automate operational workflows. Partner with business systems and platform teams to eliminate manual data handoffs and reconcile data across services, warehouses, and external systems.
• Enable insights and experimentation. Support analytics, ML, and product engineering teams by exposing high-quality, self-healing, maintainable data assets.

Data Engineer, Notifications

• 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

• Join a team of talented engineers working closely with Data Scientists to build and scale our next-generation Ad EnGage data pipeline.
• Measure and optimize the effectiveness of advertising by combining and analyzing a wide range of datasets.
• Work with large-scale datasets using a modern data stack centered on AWS, Airflow, dbt, and Snowflake.
• Contribute to building reliable, high-quality data pipelines and improving the performance, scalability, and observability of our data platform.

• The Data Engineer is responsible for data acquisition strategies and integration, scripting and tools, data migrations, conversions, purging, and back-ups.
• The incumbent works with product, financial control, analysts, users, and other stakeholders to understand business requirements and support data architecture to build data acquisition strategies.
• The Data Engineer authors artifacts defining standards and definitions for storing, processing, and moving data including associated processes and business rules.
• Additionally, the Data Engineer maps the details within these their artifacts to business processes, non-functional characteristics, QA criteria and technical enablement.
• He/she constantly thinks through the needs of the business to support efficient and error-free processes.
• Finds trends in datasets and develops workflows and algorithms to make raw data useful to the enterprise.
• Designs and implements standardized data management procedures around data staging, data ingestion, data preparation, data provisioning, and data destruction (scripts, programs, automation, assisted by automation, etc.).
• Ensures quality of technical solutions as data moves across Healthfirst environments.
• Provides insight into the changing data environment, data processing, data storage and utilization requirements for the company and offer suggestions for solutions.
• Ensures managed analytic assets support Healthfirst’s strategic goals by creating and verifying data acquisition requirements and strategy.
• Develops, constructs, tests and maintains architectures.
• Aligns architecture with business requirements and uses programming language and tools.
• Identifies ways to improve data reliability, efficiency, and quality.
• Conducts research for industry and business questions.
• Deploys sophisticated analytics programs, machine learning and statistical methods.
• Prepares data for predictive and prescriptive modeling and finds hidden patterns using data.
• Uses data to discover tasks that can be automated.
• Creates data monitoring capabilities for each business process and works with data consumers on updates.
• Aligns data architecture to Healthfirst solution architecture; contributes to overall solution architecture.
• Helps maintain the integrity and security of the company data.

• Aligning future state enterprise information architecture with corporate strategy
• Collaborates with enterprise architects and solution architects
• Supports enterprise data architecture roadmap by managing architectural runway
• Provides data architecture thought leadership
• Establishes collaborative, cross-functional relationships

Enterprise Data Architect

• Build and own data marts spanning operational, advertising, and telemetry data — designed for analytics, reporting, AI, and operational use cases
• Ingest and process large-volume event data from client apps, ad tech platforms, stitcher services, ad servers, and telemetry pipelines
• Clean, harmonize, and integrate data across systems with different schemas, identifiers, grains, and timing — producing conformed dimensions and shared definitions (users, sessions, devices, content, campaigns, impressions)
• Stitch identity and sessions across client, server, and ad-side events to enable accurate user, content, and revenue analytics
• Troubleshoot data incidents end-to-end — from a dashboard anomaly back through marts, transformations, and raw event logs — and drive permanent fixes
• Build, support and improve visualizations in partnership with analysts and stakeholders, ensuring dashboards are accurate, performant, and trusted
• Establish data quality standards — testing, monitoring, alerting, freshness and volume SLAs — so issues are caught before stakeholders see them
• Document datasets, lineage, and business logic so consumers across analytics, product, and ad ops can self-serve with confidence
• Partner closely with analysts, data scientists, ad ops, product, and source-system owners to translate business questions into durable data models
• Develop/Improve new or underutilized data sets internally and externally
• Analyze complex and huge datasets to o understand patterns and develop actionable insights o develop new initiatives to improve business KPIs such as usage, revenue, etc. o define new metrics and KPIs to track new initiatives
• Work closely with all business functions to enable transparent data-based decision making.
• Contribute to the daily variance identification across multiple platforms.
• Drive complex strategic projects investigations and analysis.
• Work cross functionally on enterprise-wide programs with Engineering, Broadcast Operations, Finance, BI and Data Engineering teams to improve performance and profitability.
• Research and share information on the latest tools and best practices.
• Mentor engineers and analysts on SQL, modeling, event data, and engineering best practices.

Senior Data Engineer

• Lead the design and evolution of the data platform architecture, establishing patterns and standards the team builds on.
• Build and operate production-grade data pipelines that ingest and transform high-variance, real-world clinical data reliably and at scale.
• Design for automation from the start: pipelines that detect problems, recover gracefully, and surface issues without requiring manual intervention to run.
• Contribute to quarterly data product releases, working closely with product, clinical, customer success teams to meet commitments.
• Build data quality tests that reflect the evolving needs of our downstream consumers.
• Mentor and elevate other data engineers through code review, architecture decisions, and shared standards.
• Actively use and advocate for AI tools that improve the team's development velocity and code quality.

• 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

• Support the design, development, and implementation of trade lifecycle data solutions.
• Build cloud-native data pipelines.
• Onboard complex financial data sources.
• Support a unified trade architecture that enables trade processing, reconciliation, settlement, and exception management functions.

• Architect and develop robust, scalable ETL/ELT pipelines using Cloud Dataflow, Cloud composer (Airflow), and Pub/Sub for both batch and streaming use cases
• Leverage BigQuery as the central data warehouse and design integrations with other GCP services (e.g., Cloud storage, Cloud functions)
• Build and optimize analytical data models in BigQuery
• Implement partitioning, clustering, and materialized views for performance and cost efficiency
• Ensure compliance with data governance, access controls, and IAM best practices
• Develop integrations with external systems (APIs, flat files etc.) using GCP-native or hybrid approaches
• Utilize tools like Dataflow or custom Python/Java services on Cloud Functions or Cloud Run to handle transformations and ingestion logic
• Build automated CI/CD pipeline using Cloud Build, GitHub Actions, or Jenkins for deploying data pipeline code and workflows
• Set up observability using Cloud Monitoring, Cloud Logging, and Error Reporting to ensure pipeline reliability
• Lead architectural decisions for data platforms and mentor junior engineers on cloud-native data engineering patterns
• Promote best practices for code quality, version control, cost optimization, and data security in a GCP environment
• Drive initiatives around data democratization, including building reusable datasets and data catalogs via Datapelx or Data Catalog

• contribute to the development and deployment of AI and machine learning infrastructure
• Design, develop, and maintain ELT/ETL pipelines
• Build and optimize data models in cloud-based data warehouses
• Architect and provision cloud data infrastructure on GCP
• Ensure all data systems comply with HIPAA and HITECH regulations
• Implement data quality frameworks and validation rules
• Partner with data scientists, analysts, and ML engineers

• Act as a self-starter who drives execution independently, taking ownership and initiative with minimal need for day-to-day direction.
• Lead high-visibility RWE projects, starting with claims data, and keep multiple initiatives moving by proactively unblocking teams.
• Own the end-to-end architecture for critical data assets, ensuring solutions are scalable, reliable, and aligned with H1’s long-term vision.
• Design, build, and optimize large-scale data pipelines (hundreds of TBs) for performance, reliability, and cost efficiency.
• Partner with Product, Data Science, and downstream engineering teams to align priorities, manage dependencies, and deliver high-value outcomes.
• Represent engineering in cross-functional forums, shaping roadmaps and reducing reliance on senior leadership for day-to-day decisions.
• Develop deep domain expertise and mentor other engineers, helping raise the technical bar and influence the evolution of our data products.

Staff Data Engineer

Staff Data Engineer – Data Lake

• Design, build, and maintain scalable ERP data pipelines for platforms such as Workday and enterprise systems
• Own end‑to‑end lifecycle of ERP data pipelines (ingestion → transformation → delivery)
• Support data conversion and migration initiatives (e.g., M&A, system integrations)
• Ensure pipeline reliability, recoverability, and stable production operations
• Troubleshoot pipeline failures and resolve root causes of data defects and operational issues
• Partner with business analysts and stakeholders to translate requirements into data solutions
• Implement and enforce SDLC best practices (version control, code reviews, CI/CD, release management)
• Develop and maintain data quality controls (freshness, completeness, anomaly detection, schema management)
• Detect and manage schema drift and downstream data issues proactively
• Build and maintain monitoring, alerting, and observability for data pipelines
• Optimize performance, scalability, and cost of data workloads
• Track operational KPIs (e.g., pipeline success rates, SLAs, incident trends, recovery times)
• Create runbooks, documentation, and audit‑ready operational processes
• Collaborate with data governance, security, and compliance teams on controls and data access
• Support analytics, reporting, and integration teams with reliable, well‑governed datasets
• Communicate effectively with technical and non‑technical stakeholders and escalate risks early

Senior ERP Data Engineer

• Work with a cross-functional team to optimize and implement our data strategy with a focus on optimization for digital transformation and the use of AI/ML
• Design and document end-to-end data architectures that support diverse analytic, operational, and research needs.
• Facilitate the implementation of a modern data platform (e.g. Snowflake, Databricks, etc.)
• Identify opportunities and implement solutions to increase data interoperability and standardization among systems and across other business units.
• Develop and implement pipelines to monitor and improve both internal and external (i.e., from CRO partners) data quality.
• Work with informatics and AI engineers optimizing the utility of data for their respective pipelines.
• Monitor and optimize the performance of data architectures and platforms.
• Develop or implement critical metrics to measure the impact of the overall data strategy.
• Stay up to date with the latest advances in the field and, as appropriate, evaluate them for adoption

Senior Manager, Artificial Intelligence Data Engineer

• 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.

• Collaborate with cross-functional teams to understand business requirements and translate them into effective cloud-based data architecture solutions.
• Design data models, data flow diagrams, and schema structures that optimize data storage, retrieval, and processing on cloud platforms.
• Design and develop data integration strategies to consolidate data from diverse sources into a unified and coherent format.
• Evaluate and select appropriate cloud services and tools for various data-related tasks.
• Work closely with data engineers, data scientists, and other stakeholders to understand their needs and provide architectural guidance.
• Responsible for overseeing the successful execution of data projects and ensuring the outcomes meet business objectives and technical requirements.
• Lead and mentor junior team members, promoting knowledge sharing and continuous learning.

Senior Data Architect

• Specialize in turning raw data into business-ready products within GCP
• Design and optimize data models, marts, and semantic layers in BigQuery
• Enable analytics, BI, and ML use cases across the enterprise
• Support downstream systems and APIs that deliver trusted data for operational and AI-driven processes
• Build the pipelines, products, and platforms that power the next generation of digital distribution

See all jobs on JobTailor

Staff / Principal Data Engineer

Data Engineer, AI

Data Engineer, Notifications

Senior Data Engineer, Data Platform Admin

Data Engineer

Data Engineer

Enterprise Data Architect

Senior Data Engineer

Senior Data Engineer

Supply Chain Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Staff Data Engineer

Staff Data Engineer – Data Lake

Senior ERP Data Engineer

Senior Manager, Artificial Intelligence Data Engineer

Data Engineer

Senior Data Architect

Data Engineer – Data Products, Delivery