Data Engineer

• Build advanced data pipelines utilizing the Medallion Architecture to create high-quality data sources in Snowflake.
• Leverage AI-assisted development tools to reduce manual pipeline work and explore emerging Snowflake capabilities like Cortex to support AI/ML-enabled workflows.
• Build Data Applications that contribute to interactive experiences, including Streamlit-based apps, transforming Snowflake from a warehouse into a full-service data platform.
• Create robust data pipelines using AWS technologies to serve specific business needs.
• Design and evolve analytical data models that support BI, advanced analytics, and AI workloads.
• Partner directly with Analytics and Data Science teams to translate complex business questions into well-structured, modular data assets.
• Write complex Python/SQL, scripts, and stored procedures to ensure 99.95% uptime and optimize query performance for downstream consumption.
• Serve as a primary advisor to the Data Management team to identify opportunities for technical improvements and automation.
• Act as a leader for junior and mid-level engineers, providing direction on programming best practices, query optimization, and business tact.

Senior Data Engineer – Modern Data & AI Platforms

• Design, build, and maintain scalable data pipelines across internal databases, vendor systems, and our data warehouse
• Support both batch and event-driven or CDC-based data workflows
• Improve pipeline reliability, observability, and performance
• Troubleshoot issues in production and proactively solve data quality or system problems
• Build and maintain data warehouse schemas and transformation layers to support analytics and operational use cases
• Communicate clearly about implementation choices, tradeoffs, and technical constraints
• Identify opportunities to improve data workflows through better tooling, automation, and engineering practices
• Help shape scalable patterns for how data is ingested, transformed, stored, and accessed

Senior Data Engineer

• Define the end-to-end data roadmap for multilingual and multimodal AI systems, including text, speech, translation, interpretation, low-resource languages, and agentic AI workflows.
• Design and build dataset curation pipelines for training, post-training, and evaluation, including cleaning, deduplication, filtering, PII redaction, quality scoring, sampling, balancing, and versioning.
• Create annotation schemas, labeling guidelines, QA rubrics, golden datasets, and reviewer workflows for multilingual, speech, translation, and agentic AI data.
• Build evaluation datasets and benchmarks, analyze model failure modes, and translate performance gaps into targeted data improvements.
• Support post-training data workflows such as SFT, instruction tuning, preference data, RLHF/DPO-style data, reward model data, and synthetic data generation.
• Use modern annotation tools and AWS-based data infrastructure to scale secure, traceable, and compliant AI data workflows.

AI Data Strategy Engineer – Applied Scientist, LLM Data

• Build and maintain the data models and transformations that power reporting in the Skimmer product, leveraging tools like Fivetran transformations, dbt, and Sigma Computing materializations
• Build and maintain customer-facing data models and reports in Sigma Computing, embedded within the Skimmer application
• Manage and extend ingestion using Fivetran across a growing set of sources
• Develop and maintain data transformations in Python and SQL against our Snowflake data warehouse
• Partner with product and engineering to turn customer needs into reliable, performant reporting experiences
• Collaborate with application engineers to understand source systems and ensure clean, reliable data capture
• Monitor data quality, reliability, and report performance, and troubleshoot issues as they arise
• Document data models, definitions, and architecture to support a growing team

• Own the building, maintenance, and optimization of pipelines to ingest data from both operational databases and third-party tools into a data lake/warehouse.
• Architect highly efficient ingestion patterns that handle evolving data schemas and high-volume, multi-source data streams seamlessly.
• Optimize pipeline performance to ensure maximum uptime, high throughput, and cost-effective compute usage.
• Use dbt to transform raw data within the data warehouse into structured, production-ready schemas.
• Write templated SQL and Jinja code to enforce macro-driven, modular, and DRY (Don't Repeat Yourself) development practices.
• Perform rigorous data quality checks by implementing native dbt tests.
• Set up monitoring and alerting frameworks around both ingestion routines and dbt builds.
• Track pipeline health metrics to measure and report on overall data freshness and platform reliability.
• Build data applications that interact with the data warehouse to empower decentralized self-service analytics.
• Build internal tooling and libraries to facilitate analytics and ML work.

Staff Data Engineer

• Designs and develops moderately complex queries, objects, ETL packages, reporting structures, and other objects necessary to support detailed analysis and self-service reporting needs.
• Collaborates with analytics and business teams to create and improve data models that feed BI tools, increasing data accessibility, and fostering data-driven decision making across the organization.
• Takes ownership of the health, reliability, and security of the data platform by designing and implementing processes and solutions that proactively monitor data delivery across the full pipeline — ensuring data is accurate, complete, consistent, and timely from source ingestion through gold layer curation.
• Investigates, diagnoses, and resolves complex data pipeline failures across all system layers; uses findings to drive pipeline improvements and communicates status, root cause, and resolution clearly to the team throughout the full lifecycle of an incident.
• Assists with the development and delivery of data solutions, including data pipelines, API integration, and other structures and processes to support the data platform
• Identifies, designs, and implements internal process improvements, including the automation of manual processes, optimization of data delivery, and refactoring of code for improved performance
• Builds pipelines to enable the optimal extraction, transformation, and loading of data from a wide variety of data sources
• Provides strategic assistance to BI leadership and the BI team by making recommendations in the areas of data management and best practices
• Collaborates with developers, database administrators, data architects, and others on reporting and data management projects as needed
• Takes on other duties needed to help drive our Purpose, fulfill our Brand Principles, and abide by our Organization’s Values

Data Engineer II

• Lead a distributed engineering team across platform/product engineering, connectors, QA, DevOps/infra, AI implementation, data governance, and support.
• Own delivery, quality, release discipline, and execution of the technical roadmap.
• Install engineering discipline where it's thin: automated testing, QA, CI/CD, release governance, and versioning.
• Drive repeatable, perimeter-safe deployments, including containerization, infrastructure-as-code, secure deployment, and SOC 2 readiness.
• Partner with the Chief Architect on the connector framework, canonical metadata model, architecture decisions, and product IP.
• Build the team: assess current talent, retain the strong, hire the gaps, and align the team plan to the roadmap.
• Push practical AI use across coding, review, testing, ops, and engineering productivity.

Director of Engineering – Data Platform

• Lead the design and evolution of Foley’s enterprise data architecture
• Contribute to the ongoing development of our Enterprise Data Model
• Define standards and best practices for data modeling, storage, access, and reliability
• Drive architectural decisions that balance scalability, maintainability, and performance
• Design, build, and maintain scalable data pipelines across internal systems, vendor platforms, and the data warehouse
• Improve data quality, observability, and reliability across core workflows
• Optimize data workflows for efficiency and long-term operational health
• Support both batch and event-driven or CDC-based pipeline patterns
• Own schema design and warehouse modeling strategy, including dimensional and normalized approaches where appropriate
• Help define and implement data governance best practices, including RBAC
• Ensure shared definitions, documentation, and trust in core business data
• Serve as a thought partner to Engineering, Product, Analytics, and business teams
• Translate technical complexity into clear recommendations and tradeoffs
• Raise the bar for data engineering practices across the organization
• Help the company make better decisions through strong, accessible data foundations
• Identify opportunities to improve workflows through automation, tooling, and thoughtful use of AI
• Experiment pragmatically with new technologies and scale what works
• Build systems and guardrails that enable teams to move faster without sacrificing quality

• Architect/design solutions that align with industry best practices, future business/technology strategy and AWG’s approved technology stack that address the customer’s needs.
• Collaborate with customers to understand their business problems or needs and desired outcomes.
• Translates business context to technology solutions that meets organization objectives.
• Identification and recommendation of appropriate architecture/solutions, estimating the work efforts, upgrades, replacements, or decommissioning options incorporating business and technology productivity, usability, and total cost of ownership.
• Develops /maintains Industry standards, guidelines and best practices for the core domain.
• Participate in Architecture Review Board, conduct architectural and code reviews.
• Drive activities that take place during solution ideation, solution design, and solution implementation.
• Champions a structured approach to business application solutions and supports the architectural aspects of all application projects in peer IT portfolios.
• Communicates the architecture to the stakeholders, collaborates and coordinates with existing domain architects in the formalization and adoption of IT standards and procedures.
• Assist core/project team in solving key/major production support issues, define/develop and execute architecture optimizations based on the unknown/unexpected support issues.
• Serve as the subject matter expert and go-to resource for the functional domain and drive major/key support challenges to successful solutions as appropriate.
• Recommends and drive implementations of system enhancements to provide operational reliability and integration ensuring the business with consistent systems availability and quality.
• Responsible for overall system-wide resiliency, scalability, stability and operational efficiency of solutions
• Participates and contributes to the development of IT Roadmap aligning with business and IT strategy
• Development of technical visioning plan in areas of expertise considering future business needs and emerging technologies
• Strategically aligns with the organization's vision and drives leadership to transformation for a better business outcome.
• Responsible for the lifecycle of solutions, from selection, proof of concept including software currency, upgrades and/or technology retirement.
• Evaluate technology acquisitions designed to optimize feature/function for long term supportability, vendor viability and total cost of ownership.
• Responsible for assisting in the establishment of an IT Architecture practice, and the adoption of an architecture and standards methodology.
• Guide, Mentor and Lead Junior/emerging architects and developers.
• Drive resolution with technology vendors when support is needed.
• Influences change to standards when necessary.

Data Architect

• Develop and implement scalable, secure, and interoperable data systems (including data warehouses, data lakes, and real-time processing platforms) tailored for OU Health’s enterprise data analytics platform.
• Define and maintain conceptual, logical, and physical data models to standardize data across clinical, research, and operational systems.
• Ensure models support healthcare-specific requirements (clinical, claims, provider, member, etc.).
• Design and oversee data integration processes (ETL/ELT pipelines) to enable seamless data flow between OU Health and other entities as needed.
• Work closely with clinical, research, and administrative stakeholders to gather requirements and deliver data solutions that meet departmental needs.
• Facilitate effective communication and collaboration among all parties.
• Monitor and optimize database performance, troubleshoot issues, and integrate new technologies to support evolving healthcare analytics needs.
• Implement robust security and backup procedures to protect sensitive patient information and ensure compliance with privacy laws.
• Prepare and maintain system documentation, data dictionaries, and architecture blueprints.
• Provide training and guidance to team members on data management best practices.
• Identify opportunities to enhance data infrastructure, support innovation, and enable advanced analytics for improved patient outcomes and operational efficiency.
• Work closely with clinical staff, IT professionals, data analysts, and researchers to understand data requirements, workflows, and pain points.
• Facilitate regular meetings and workshops to gather input and align data solutions with clinical and operational needs.
• Act as a liaison between technical teams and non-technical stakeholders, translating complex data concepts into actionable insights.
• Ensure that data architecture decisions reflect the priorities of clinicians, administrators, and compliance officers.
• Promote a culture of knowledge sharing by documenting data models, standards, and best practices.
• Provide training and mentorship to team members on data management, security, and governance.
• Lead collaborative efforts to troubleshoot data issues, optimize workflows, and implement new technologies.
• Encourage open communication and feedback to continuously improve data systems and processes.
• Participate in project planning and execution, ensuring that data architecture aligns with project goals, timelines, and resource constraints.
• Coordinate with project managers and other architects to deliver integrated solutions.

Enterprise Data Architect

• 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 the content architecture strategy across platforms — detection frameworks, parser standards, playbook taxonomy, and dashboard design
• Lead platform-specific content roadmaps (Google SecOps, Sentinel, AWS) aligned to customer coverage targets
• Serve as the senior technical escalation point for all Security Solution Engineer — SIEM/SOAR technical challenges — including parsers, detection logic, SOAR automation, API integrations, and custom scripting
• Partner with the FDE and SE&A teams to translate customer-specific findings into reusable, scalable content
• Evaluate new data sources, integrations, and vendor partnerships for content expansion
• Drive adoption of AI-assisted content generation tooling and establish quality review standards
• Mentor Security Solution Engineers — SIEM/SOAR and contribute to hiring and technical growth of the team

Forward Deployed Data Architect

• 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

• Define and implement enterprise-wide AI data architecture strategies to enable secure, scalable, and compliant AI/ML adoption across the organization.
• Design canonical data models, metadata frameworks, and pipelines to support AI/ML model development, training, deployment, and monitoring.
• Establish standards for data quality, lineage, master data management (MDM), and “golden record” frameworks to ensure reliable AI outputs.
• Collaborate with data governance, compliance, and security teams to enforce policies for ethical and responsible AI data usage.
• Architect and oversee AI-ready data platforms (cloud, hybrid, and on-prem) to integrate structured, unstructured, and streaming data sources.
• Implement modern data architectures (e.g., data mesh, data fabric, Lakehouse, Apache Iceberg/Delta Lake) to accelerate AI/ML projects.
• Optimize AI/ML workloads on cloud platforms (AWS, Azure, GCP) and manage data pipelines using tools like Kafka, Glue, Spark, and Snowflake.
• Partner with AI engineers, data scientists, and business leaders to align data architecture with financial products, regulatory compliance, and risk management needs.
• Provide architectural leadership in modernization efforts (real-time payments, fraud detection, personalization engines, and regulatory reporting).

Data Engineer jobs in Kansas

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Senior Data Engineer – Modern Data & AI Platforms

Senior Data Engineer

AI Data Strategy Engineer – Applied Scientist, LLM Data

Data Engineer

Staff Data Engineer

Data Engineer II

Director of Engineering – Data Platform

Staff Data Engineer

Data Architect

Enterprise Data Architect

Supply Chain Data Engineer

Forward Deployed Data Architect

Data Engineer

AI Data Architect