Data Engineer

• Align data solutions to business requirements by planning and managing data infrastructure and strategy for our AI/ML Maintenance, Sustainment, and Deployment Planning Project.
• Enhance and optimize data entry, management and extraction within databases to ensure usability within proprietary systems.
• Perform data quality checks, analysis, present data and document processes.

Senior Data Engineer

• Serve as the operational backbone of Kobie's data platform, ensuring the ETL/ELT pipelines, dimensional models, and Snowflake environments that power client reporting and dashboards run reliably, at scale, every day.
• Translate client loyalty program requirements into production-ready data warehouse structures — from source system analysis and dimensional modeling through to the KALC (Kobie Alchemy Loyalty Cloud) platform tables and extracts that downstream reporting and client services teams depend on.
• Diagnosing and resolve data pipeline failures quickly enough that clients and internal stakeholders rarely notice they happened by bringing the debugging fluency and Snowflake depth to contain impact and prevent recurrence.
• Build a data architecture that can absorb change by redesigning Kobie's data mart to support an incoming event-driven, domain-driven application layer without breaking the operational foundation already in production.
• Deliver the data infrastructure that makes AI-powered loyalty products possible. Not by building the models, but by building the reliable, well-governed platform that Kobie's innovation team needs to produce them.
• Leave every pipeline more trustworthy than you found it, through automated testing, audit logging, CI/CD discipline, and documentation that makes the platform easier for the next engineer to own.

• 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

• Lead the design and architecture of our most critical data pipelines and data platform components
• Set technical standards and best practices that elevate the entire data team
• Mentor and support junior and mid-level engineers through code reviews, pair programming, and knowledge sharing
• Own the health, scalability, and reliability of our orchestration infrastructure
• Partner with engineering, analytics, and leadership to shape the roadmap for our data platform
• Evaluate and introduce new tools and technologies to keep our stack modern and effective
• Ensure compliance and confidentiality of Personal Health Information across all data

• Design, develop, and maintain production-grade Python and Java data services and pipelines deployed on Google Cloud Platform.
• Build and evolve Google Cloud Dataflow batch and streaming pipelines for data ingestion, standardization, curation, and analytics load.
• Implement and maintain event-driven data workflows using Google Cloud Pub/Sub.
• Design and manage BigQuery datasets, table schemas, partitioning strategies, clustering, and reporting views.
• Build and maintain Cloud Composer (Apache Airflow) DAGs for workflow orchestration.
• Develop and maintain Cloud Run microservices and Cloud Functions.
• Participate in design and code reviews; mentor junior engineers and contribute to shared coding standards.
• Collaborate with teams to ensure on-time delivery and best engineering practices.
• Engage proactively in the triage and resolution of escalated production issues.
• Follow and comply with all security policies and procedures established by the organization, including adherence to HIPAA and HITRUST regulations.

Senior Software Engineer, Data Platform

• Define and evolve the technical vision and roadmap for APFM's Databricks platform and broader data ecosystem.
• Drive the evolution of APFM's federated data operating model, balancing domain ownership with enterprise governance and standards.
• Partner with Analytics Engineering and business stakeholders to evolve the semantic layer, ensuring business definitions, metrics, and dimensions remain consistent, discoverable, and trusted.
• Establish standards for metadata management, lineage, discoverability, stewardship, and data quality across the organization.
• Drive development of reusable data products and shared platform capabilities that enable self-service analytics at scale.
• Lead adoption and governance of Databricks capabilities including Delta Lake, Unity Catalog, Workflows, AI/BI Genie, MLflow, Vector Search, and related platform services.
• Partner closely with AI and Machine Learning teams to establish scalable operational patterns for model development, deployment, monitoring, and governance.
• Guide technical decision-making across multiple teams and domains, helping align platform investments with business outcomes.
• Mentor engineers across the organization and help elevate technical capabilities and engineering excellence.

Principal Data Engineer, Data Platform

• Drive the company-wide AI agenda: scaling local AI initiatives into centralized production systems that create measurable business value.
• Own broad, high-impact AI initiatives that have cross functional impact: Moving our AI usage from past narrow or siloed automation examples to execute a company operational transformation.
• Partner with Product, Engineering, and operational leaders to embed AI capabilities into our customer-facing platform, internal business workflows, and core processes.
• Serve as the ultimate technical authority for our data and AI infrastructure, making critical architectural decisions across MLOps pipelines, LLM orchestration frameworks, and distributed data systems.
• Oversee the development of a scalable corporate data platform that serves as the foundational bedrock for all AI and machine learning capabilities.
• Transform our internal knowledge base by building the systemic data architecture needed to turn unstructured information into intelligent, easily searchable, and actionable assets.
• Re-build and evolve our company-wide data structures and connectivity pipelines to be optimized to deploy, run, and maintain AI models efficiently while lowering cost-to-serve.
• Set the company wide tooling standards, maintain technical quality guardrails, and establish engineering best practices for embedded AI specialists working within distinct business units.
• Actively support the implementation of key local solutions built by decentralized specialists, ensuring they have the tools and centralized platform support required to succeed.
• Maintain high levels of ethical compliance by ensuring all global AI initiatives strictly adhere to data privacy, platform security, and ethical compliance standards.
• Educate and empower teams across the organization to scale their own AI use productively, providing them with the structural frameworks needed to safely build and innovate within their functions.
• Partner deeply with the People and Operations functions to champion organizational AI capability, replacing operational uncertainty with structured technical training loops that convert manual specialists into power-users of automated solutions.
• Drive the organizational change management and literacy efforts required to reshape daily workflows, shifting company culture and habits from traditional manual processes to AI-assisted operations.

Senior Director, Data Platform and AI

• Design and implement ETL processes in a cloud environment
• Design and implement monitoring solutions for ETL processes
• Document datasets for data warehouse consumers
• Serve as a technical resource for consumers of the data warehouse, in particular the Data Science Team and other internal business units

• Run and monitor recurring data update and ingestion jobs.
• Perform basic data editing, formatting, and normalization in SQL-based workflows.
• Support the transformation of raw or external data into usable internal formats.
• Assist in data validation, anomaly tracking, and issue escalation.
• Document standard workflows for future execution and training.

Data Engineer 1, Data Services

• Design, build, and maintain scalable data ingestion pipelines supporting a wide range of source systems (APIs, databases, streaming platforms, third-party data providers)
• Develop batch and real-time ingestion frameworks capable of handling high data volumes and low-latency requirements
• Build fault-tolerant and resilient pipelines with strong guarantees around data integrity, idempotency, and recovery
• Establish and enhance observability, monitoring, and alerting for ingestion pipelines (latency, throughput, failures, data freshness)
• Implement and enforce data quality checks and validation frameworks at ingestion points
• Contribute to the development of Data Ingestion as a platform product, including reusable frameworks, standards, and best practices
• Leverage AI-assisted development tools and automation to accelerate pipeline development, improve code quality, and enhance operational efficiency (e.g., code generation, testing, anomaly detection, workflow automation)
• Partner with upstream system owners and downstream consumers to define data contracts, SLAs, and schema evolution strategies
• Optimize pipelines for performance, cost, and efficiency across compute and storage layers
• Use Infrastructure as Code (IaC) to provision and manage ingestion infrastructure in a consistent and scalable manner
• Collaborate with platform, analytics, and data science teams to ensure seamless data availability and usability.

• 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 pipeline and infrastructure work from intake through production
• Build and maintain ingestion and transformation pipelines from operational systems, third-party APIs, and event streams into the warehouse
• Turn product and business requirements into specifications and well-modeled datasets
• Write and maintain Terraform for the AWS resources your pipelines run on
• Use CI/CD to ship pipeline and infrastructure changes safely and often
• Build data quality checks, unit tests, and integration tests for the pipelines you own
• Use modern tooling, including AI assistants, to move faster on development, testing, and documentation
• Build datasets and reporting layers for product features, BI, and operational reporting
• Work with Product, Engineering, Operations, Sales, and Compliance so data solutions actually fit the downstream use case
• Write the runbooks and documentation that on-call teammates and stakeholders will rely on
• Watch your pipelines in production and fix issues before someone has to ask
• Keep up with what is changing in data engineering, cloud infrastructure, and fintech compliance

• Lead the implementation of AI data pipelines integrating structured, semi-structured, and unstructured data to support AI and agentic solutions
• Develop AI-driven data systems that enhance data capabilities while ensuring adherence to industry best practices
• Implement and optimize Retrieval-Augmented Generation (RAG) architectures and integrate them with enterprise data platforms
• Design, build, and optimize scalable batch and streaming data pipelines with a focus on performance, resiliency, and operational efficiency
• Develop and maintain real-time data streaming pipelines using technologies such as Snowpipe
• Develop data domains and data products to support reporting, analytics, AI/ML, and data science use cases
• Ensure the reliability, availability, and scalability of data pipelines through monitoring, alerting, and incident management
• Implement reliability engineering best practices, including fault tolerance, redundancy, and disaster recovery
• Drive engineering discipline across data platforms, including observability, data quality, lineage, and governance
• Collaborate with DevOps and infrastructure teams to enable seamless deployment and operation of data systems
• Provide architectural leadership in partnership with Data Architects, including defining technical standards and influencing enterprise-wide practices
• Develop and integrate graph database solutions to support complex data relationships within AI systems
• Apply GenAI approaches to insurance-specific data use cases and challenges
• Lead the development of AI-ready data foundations that support scalable, production-grade solutions
• Ensure data platforms remain resilient, governed, and cost-efficient, aligned with enterprise cloud and data strategies
• Mentor junior engineers and contribute to communities of practice, promoting best practices, reusable patterns, and engineering standards
• Stay current with advancements in GenAI and apply relevant technologies and methodologies to platform evolution.

Senior Staff AI Data Engineer

• Collaborate closely with the development team to deploy and maintain application infrastructure.
• Assist in the development and support of tooling to streamline the deployment and maintenance of our products.
• Work with Kubernetes, Docker, and Helm to deploy applications from development through to production environments.
• Support both in-house and third-party applications, including handling deployments, upgrades, and troubleshooting.
• Write and manage automation pipelines for application deployment and maintenance.
• Engage in the day-to-day management of Linux servers via the command line.
• Document processes and best practices clearly and concisely.
• Assist in deploying, monitoring, and maintaining data pipelines, ETL/ELT processes, and data infrastructure.
• Collaborate with data engineers to support data workflows and ensure timely and accurate data delivery.

DataOps Engineer

• Design, build, and productionize reliable, scalable, and secure data pipelines and data products in Azure Databricks that support AI, analytics, and operational use cases.
• Lead the transformation of raw data from multiple source systems into trusted, well-structured datasets suitable for downstream analytics, model development, and AI enablement.
• Drive improvements in internal processes, delivery patterns, and technical solutions that support broader functional strategy and increase team efficiency.
• Solve complex and non-standard data engineering problems using advanced analytical and problem-solving techniques, with strong ownership and sound technical judgment.
• Partner closely with data scientists, analysts, software engineers, product stakeholders, and business teams to ensure data is accessible, trustworthy, and aligned to business outcomes.
• Support AI and data science use cases by enabling high-quality feature-ready data, experimentation workflows, and scalable patterns for model development and deployment.
• Contribute to technical direction within the team, influence key engineering decisions in their area, and act as a go-to resource for peers and less experienced engineers.
• Mentor team members through technical guidance, knowledge sharing, and strong engineering practices consistent with Level 7 expectations.

• Develops, tests, and maintains complex prioritized engineering (design/build/test) work as a member of a data analytics product pipeline architecture and infrastructure team
• Investigates and analyzes feasibility of data integration prototypes and program requirements
• Develops specifications and plans the full range of programming actions to produce data integration components
• Develops and maintains complex automated ETL/ELT pipeline architecture that cleans, transforms, and aggregates data from disparate sources using architecturally approved tools, programming languages, and services to deliver a data mesh approach
• Develops and maintains complex automated ETL/ELT monitoring and alarming solutions using architecturally approved languages and services
• Monitors, troubleshoots, and optimizes data pipelines for quality, performance, and reliability creating a single source of truth providing business customers with highly reliable and quality data
• Builds complex unit tests to enable test driven development and automation to drive code reliability
• Develops, maintains, tests, and deploys code with architecturally approved toolsets using Dev Ops practices, ETL/ELT pipelines, data catalogs and data queries
• Maintains data solutions in a divest state to ensure business functionality until replaced with new technology
• Balances and designs data analytics product solutions based on cost and performance to meet data needs and business requirements
• Guides business customers to develop, troubleshoot, and optimize complex SQL and ETL/ELT solutions to solve reporting, metrics, and analytics problems
• Collaborates with cross-functional teams to understand data requirements for data analytics products and translate them into scalable and efficient data solutions

Data Engineer III

• Design and implement enterprise data architectures including Data Lakes, Data Warehouses, Data Fabric, and MDM solutions.
• Build scalable data pipelines and data integration frameworks.
• Define data governance, quality, lineage, and security standards.
• Enable AI/ML initiatives through robust data engineering and architecture practices.
• Collaborate with business and technology teams to develop data strategies and roadmaps.
• Optimize data platforms for performance, scalability, and reliability.

Senior Data Architect, Data Engineer – AI & Data Platforms

• Become a healthcare insurance subject matter expert with sufficient technical skills to produce proof-of-concept solutions to various issues within the industry.
• Perform data cleaning and analysis using SQL, Python, and AI models.
• Analyze big data from diverse sources to identify trends, address gaps, and discover improvement opportunities that align with improving the healthcare system.
• Provide subject matter expertise in identifying product uses and designing strategies that address significant business and technical challenges.
• Gather and translate business requirements into analytical applications and advanced dashboard visuals, reports, or other alert systems.
• Document code using documentation tools and participate in collaborative team code reviews.
• Collaborate with various teams within the company to advise on technical solutions.
• Stay informed on industry trends, best practices, and innovations, ensuring competitive market relevance of company products.

Business 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

• Hands-on Development: Actively write, review, and optimize production-grade code (PySpark, SQL) to build scalable data pipelines, data architectures, and delta tables.
• Pipeline & Architecture Management: Architect and manage robust ELT/ETL processes within Azure Data Factory and optimize cloud data structures within Azure Databricks (Delta Lake, Unity Catalog).
• BI Infrastructure: Develop and govern semantic data models, advanced DAX queries, and enterprise-grade Power BI dashboards to deliver complex insights directly to business units.
• Technical Oversight: Supervise and directly contribute to the development of AI/ML models, data pipelines, and scalable data architectures, collaborating with data scientists, engineers, and product managers.
• Shepherd Leadership: Guide SMEs and business stakeholders in identifying business needs and developing problem statements, business rules, and metrics for strategic Data & AI initiatives.
• Team & Workload Leadership: Manage a high-performing team of data engineers and analysts, balancing resource allocation, mentoring technical growth, and overseeing daily Agile delivery.
• Scope & Estimation: Define and manage the overall scope of initiatives, gain appropriate signoff from stakeholders, and work across IT and vendor partners to develop timelines and cost estimates.
• Business Case Building: Assist leadership with building business cases, tracking budget-versus-actuals, and preparing for funding approvals and various stage-gating processes.
• Lifecycle Governance: Ensure requirements are met throughout the project lifecycle process and keep stakeholders apprised of changes to scope.
• Cross-Functional Alignment: Create strong partnerships with key leaders throughout the enterprise to identify SMEs and project customers.
• Change Agent: Drive the internal adoption of modernized modern data warehouse concepts and maintain alignment between technical output and retail business goals.

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