Job Summary  
The Product Solutions Designer on the Enterprise AI and Automation team is, at its core, a design and discovery role. You will dig into ambiguous business problems, map the processes behind them, and design the solution, both the user experience and the workflow underneath it. Design here means more than just visual polish, it means understanding how a process should work before a single screen is built.  

Once you know what needs to exist, you'll build it. You will use AI-assisted development tools and no-code platforms to move from concept to working prototype. Some solutions may stay as low-code internal tools, others will utilize team resources to scale up to full on applications, but you will own the solution end-to-end: discovery, design, and delivery.  

You thrive with autonomy. You are comfortable navigating ambiguity, making decisions with incomplete information, and delivering results without heavy oversight. You want to solve real problems. 

What You’ll Do  

• Intake and discover. Triage requests from across the organization, determine what is worth solving, and conduct the discovery work needed to understand it. This includes process mapping, stakeholder interviews, data analysis, and workflow documentation.  

• Design the solution. Own the end-to-end user experience. Create wireframes, user flows, and interface designs that are clear and functional. Design means both the visual layer and the process underneath it.  

• Build and ship. Use low-code platforms, no-code tools, and AI-assisted development to move from design to working prototype. You do not need to be a software engineer, but you need to be able to deliver something real.  

• Collaborate and scale. Work with the rest of the team to transition prototype solutions into wide-scale applications.  

• Manage your own work. Own your portfolio of projects and communicate status without needing to be closely managed.  

What We Need  

• Experience. 8+ years in roles that combine problem-solving, process improvement, and hands-on solution delivery. The specific title does not matter; the experience does.  

• Design and UX. You design interfaces that are clear, functional, and intuitive. Usability is the priority, you can take a messy business process and turn it into something people actually want to use. Proficiency in Figma or similar tools required.  

• Presence and communication. You can lead intake conversations with stakeholders across the organization, ask the right questions to understand what needs to be built, and translate what you learn into clear designs and recommendations.  

• Discovery and analytical skills. You can dig into a business process, identify where it breaks down, analyze data to understand the real problem, and articulate what needs to change.  

• Building experience. Hands-on experience with low-code platforms (e.g., Retool) and comfort with AI-assisted development tools (e.g., Claude Code, Cursor, Copilot). You do not need to be a traditional software engineer, but you must leverage these tools to ship working solutions.  

• Data fluency. You understand how databases work, can explore data in tools like Snowflake, and are comfortable using AI to generate and refine queries and pull your own insights without relying on a data team.   

Desired Qualifications  

• Technical familiarity. Exposure to data pipelines, integrations, or API-based workflows. Familiarity with JavaScript/Python at a read-and-modify level, enough to work with AI assistance, not enough to architect a platform.  

• Tooling experience. Familiarity with CRM platforms (Salesforce) and project management tools (Jira, GitHub). You will likely encounter both regularly.  

• Process and operations background. Experience with process mapping tools (Lucidchart, Miro, BPMN) and familiarity with business process automation or operations consulting.  

• Comfort with ambiguity and range. You have worked in environments where roles were not rigidly defined and you owned outcomes across multiple functions. You are energized by variety, not overwhelmed by it.  

What You'll Need to Succeed  

• Autonomy and resourcefulness. You do not wait to be told what to do. You can identify problems, propose solutions, and execute. When you do not know how to do something, you figure it out or you reach out for the right resources.  

• Range and adaptability. Some days you are a designer, some days a builder, some days an analyst. You shift between modes comfortably and do not see rigid role boundaries.  

• Bias toward action. You would rather ship something imperfect and iterate than spend weeks perfecting a plan. You know when “good enough” is the right answer, and when something needs real discovery first.  

• Willingness to teach. You are comfortable being the person people come to with AI questions, and you can explain what these tools can and cannot do without overselling or gatekeeping.  

• You lead through craft and influence — mentoring peers, establishing frameworks, and raising the bar for product excellence without a reporting structure. 

• Passion for building products that create meaningful impact in healthcare. 

Base Salary Band: $100,000-$170,000

Compensation: At PatientPoint, we are committed to providing competitive pay and benefits that are in line with industry standards. We analyze and carefully consider several factors when determining compensation, including skills, qualifications, geographic location, and professional experience, which can cause your compensation to vary. The base salary range listed is just one component of PatientPoint’s total compensation package for employees. For additional details on our total benefits package, please review the section “About PatientPoint” at the end of this job description.

Senior Data Scientist Consultant, Managed Agent Platform (MAP) – Agent Platform Team P2392 

Summary 

The Agent Platform Team, within AI Enablement at 84.51°, owns and manages the core platforms that power data science and AI across the organization. This role sits on the MAP (Managed Agent Platform) squad and is focused on helping stand up a new, centralized platform that simplifies and accelerates the design, development, deployment, and monitoring of AI agents across the organization. 

The MAP is being built in response to growing demand for a flexible, no-code/low-code solution that enables both technical and non-technical users to build and deploy agents without deep engineering overhead. The platform is early-stage — the team is actively evaluating different platforms and tooling, defining workflows, and establishing standards. You will be joining at the ground floor and helping shape what this platform becomes. 

You will be the bridge between the engineers building/maintaining the MAP and the data scientists and agent builders who will use it. You are not building agents for end-clients. Instead, you ensure the platform works for the people on it — that capabilities are tested, documented, and adopted smoothly, and that practitioner needs make it back to the engineering team in a way that shapes the roadmap. 

You’ll bring hands-on familiarity with the open-source agent ecosystem — including frameworks like LangChain, LangGraph, Google ADK, and OpenAI Agents SDK — and apply that knowledge to evaluate platform capabilities, inform design decisions, and help practitioners understand what’s possible. As the platform matures, you’ll help onboard users, develop best practices, and drive adoption across the organization. 

This is a small, engineer-heavy squad that is standing something up from scratch. Everyone pitches in on a bit of everything. Priorities will shift frequently as the platform takes shape. Success in this role is less about fixed technical expertise and more about comfort with ambiguity, good judgment about where to spend your time, and the ability to influence without direct authority. 

Responsibilities 

• Voice of the practitioner: Serve as the primary liaison between data scientists and agent builders and the MAP engineering team. Gather requirements, surface pain points, and translate practitioner needs into actionable feedback that shapes platform decisions, standards, and the roadmap. 

• Platform evaluation & definition: Help evaluate and select platform tooling and contribute to defining core platform capabilities — including agent building workflows, orchestration patterns, tool/API connectivity, observability and tracing, evaluation frameworks, and governance controls. Test and document findings, recommended use cases, limitations, and rollout considerations. 

• Onboarding & enablement: As the platform matures, write guides, maintain documentation, lead training sessions, and run office hours to help data scientists and agent builders adopt the MAP. Serve as a first line of support when teams hit roadblocks. 

• Adoption & communication: Evangelize the MAP to increase awareness and drive adoption across the organization. Produce clear communications and materials for both technical and non-technical audiences. 

• Cross-functional collaboration: Work closely with engineers, the solution architect, and product lead on the squad to ensure platform capabilities have well-defined use cases and that guidelines and best practices are established. Act as an agent-building ambassador to cross-functional teams, explaining recommended workflows and the reasoning behind platform choices. 

• Governance & compliance support: Help validate that platform capabilities meet enterprise requirements around security, compliance, observability, scalability, and data governance. 

Qualifications & Experience 

• Bachelor’s degree in mathematics, statistics, computer science, economics, or a related discipline. 

• 2–4 years of experience in data science, AI/ML development, or applied analytics. 

• 2–4 years of experience in tech consulting, retail analytics, or related professional services. 

• Hands-on experience with Python and SQL. 

• Hands-on experience with one or more agent frameworks (e.g., LangChain, LangGraph, Google ADK, OpenAI Agents SDK, CrewAI, AutoGen) is required. 

• Experience gathering user needs, documenting requirements, and driving issues to resolution with technical teams. 

• Experience partnering with software engineering and/or product management teams. 

• Familiarity with LLMs, prompt engineering, and agentic AI concepts. 

• Exposure to agent platforms is a strong plus. 

Skills 

• Strong written and verbal communication, including the ability to explain technical concepts clearly to non-technical audiences and produce high-quality documentation. 

• Ability to influence across teams and seniority levels without direct authority. 

• Comfort operating in ambiguity — this platform is early-stage, and priorities will shift frequently as the team learns what works. 

• Strong project and time management skills; able to manage multiple priorities with minimal oversight. 

• Deep curiosity about the agentic AI ecosystem and a genuine desire to become a subject matter expert on agent orchestration, evaluation, observability, and deployment patterns. 

• Comfort working on a small, engineer-heavy team where you are often the only non-engineer voice in the room. 

#LI-SSS

Our mission: to enable businesses to grow and reimagine themselves by becoming advocates and champions for their Customers. With deep heritage and expertise in retail – one of the world’s most competitive markets, with a deluge of multi-dimensional data – dunnhumby today enables businesses all over the world, across industries, to be Customer First.

Job Title: Senior Engineer - (Multiple positions available)

Job Location: 3825 Edwards Road, Suite 600, Cincinnati, OH 45209 (May work remotely)

Job Duties:

WHY I DO MY JOB | Job purpose

To contribute to the success of the engineering team by autonomously delivering on the development and deployment of new technology products and/or platforms that drive excellent solutions for clients in order to enable dunnhumby to grow. May work remotely from home office within U.S., consistent with dunnhumby’s remote work policy.

WHAT I DO | Key accountabilities

• Deliver autonomously on development and deployment of complex applications, components, software, platforms or services that are used at scale.
• Conduct requirements analysis, balance different requirements and make technical trade off decisions at application level (e.g. component design).
• Lead exciting end-to-end projects of certain complexity and ensure Service Level Agreements are successfully delivered.
• Define and develop solutions to technical problems that arise.
• Provide advice and guidance to junior colleagues as required to resolve specific challenges they are having and conduct design and code reviews.
• Identify and advise on improvement opportunities and automate and simplify test and operations processes.
• Use your expertise to input into reengineering and design.
• Act as a subject matter expert for focus areas across the technology space.

Job requirements:

• Bachelor’s degree or higher (or foreign educational equivalent) in Computer Science, Mathematics, Physics, Electronics, Engineering or related field.

In addition to the degree, must have at least 5 years of experience in each of the following:

• Software development using Agile;
• Software testing and debugging;
• Cloud infrastructure, management and hosting; and
• Infrastructure as code / DevOps using Kubernetes.

Within this, must have at least 4 years of experience in each of the following:

• Data analysis and modelling;
• Working with big data using Hadoop/HDFS; and
• Utilizing at least 2 of the following programming languages: Scala, C#/C++, Javascript / React, SQL, Python, PHP.

Callibrity is a developer owned and managed custom software development consulting company that is dedicated to creating quality software using modern technologies and adding unquestionable business value to companies across multiple industries and verticals. We are problem solvers...people who like a challenge and enjoy working with modern tech stacks. We offer an incredibly collaborative culture and enjoy solving complex problems with our clients. If you are looking for a company where you can grow in your career and that values people, transparency, and flexibility we would like to talk to you.

As a Senior Software Engineer, you are a leader by example, you are a subject matter expert, and you help lead strategic technical direction. You will get to interact with key client stakeholders on a regular basis and use your experience to prove business ROI through innovative greenfield technology solutions. Software Engineer typically have longer tenures and higher retention rates with Callibrity than other companies because the work is challenging and rewarding, we have a strong engineering culture, and we invest in the best people.

Responsibilities:

• Are trusted advisors to our clients and seen as thought leaders in the tech industry
• Are keenly aware of big picture business problems and goals and capable of a leadership role
• Mentor, train, and upskill your Callibrity peers and clients’ teams
• Can solve a wide variety of problems with the appropriate solution drawn from a large subset
• Communicate complex ideas effectively, both verbally and in writing
• Proactively identify problems, challenges, and issues and recommend the best possible solution
• Demonstrate mastery and cable to teach software development best practices (Agile, DevOps, Test Automation, CI/CD, TDD) to others
• Stay ahead of the curve by exploring new technologies including AI, Machine Learning, Cloud, and IoT
• Thrive in an environment where there is close interaction among different roles and teams
• Are admired by the client as someone they can count on, regardless of title or seniority

Requirements:

• 5+ years of experience with web application development and/or SaaS platforms.
• Extensive senior-level development experience with object-oriented languages in Python, FastAPI/Flask, or Java
• Proven experience leading the technical delivery of complex and innovative business functionality.
• Strong understanding of software engineering principles such as design patterns, algorithms, and data structures, and demonstrable application of each in recent projects.
• Cloud experience with AWS, Azure, and/or Google Cloud Platform preferred
• Experience with Docker, Kubernetes, and/or developing applications for a major public cloud provider is a proactive owner who takes accountability for code from development through production.
• Strong knowledge of performance engineering and performance testing.
• Experience with integration technologies (RESTful APIs, messaging systems, etc.)
• Relevant professional experience in Information Technology and/or Consulting
• Four-year degree or successful completion of respected bootcamp/accelerator, or extensive mentorship program
• Successfully solve Callibrity’s CoderPad and Code Pairing Challenges
• Interpersonal skills: Influencer, professionalism and accountability, passion for learning, us not them mentality, shared responsibility
• Embodies Callibrity’s Core Values
• Must be authorized to work in the United States, without sponsorship

Benefits of Being a Callibrity Consultant:

• Health/Vision/Dental insurance effective first day of employment
• 401k match vested immediately
• Profit Sharing Bonus
• Annual professional development stipend ($1,500)
• Paid parental leave
• Paid bench time
• Paid flex time for training, blogging, speaking engagements, and thought leadership
• Paid time off (3–5 weeks depending on level) + 8 holidays
• Competitive industry salary
• Lunch and learns
• Team building events
• Small company feel with the stability of a proven business model
• High employee retention and satisfaction (One of Cincinnati’s Best Places to Work!)

Callibrity is based in Cincinnati, Ohio. This role is a full-time hourly, hybrid position and only candidates within commuting distance to Cincinnati will be considered. 

Callibrity is a developer owned and managed custom software development consulting company that is dedicated to creating quality software using modern technologies and adding unquestionable business value to companies across multiple industries and verticals. We are problem solvers...people who like a challenge and enjoy working with modern tech stacks. We offer an incredibly collaborative culture and enjoy solving complex problems with our clients. If you are looking for a company where you can grow in your career and that values people, transparency, and flexibility we would like to talk to you.

We’re seeking a Software Test Engineer who is diligent, analytical, and passionate about improving product quality from end to end. In this role, you will work closely with our software engineering teams to craft, maintain, and evolve scalable, reliable testing frameworks and processes. You’ll take ownership of test inventory, strengthen test strategies, and help prevent poor user experiences by actively challenging assumptions and identifying quality risks early. This role is ideal for someone who enjoys functional testing, debugging, exploratory testing, and contributing to the broader quality culture of the team.

Responsibilities:

• Work on problems of diverse scope and perform detailed analysis where required
• Take ownership of test coverage, test inventory, and overall test quality
• Independently design, develop, and execute test plans for complex features
• Build and maintain end-to-end test scenarios across multiple product components
• Conduct exploratory and ad-hoc testing without step-by-step guidance
• Troubleshoot and triage product issues, collaborating with engineers and PMs
• Support automation engineers by providing functional insights and test scenarios
• Assist with identifying and diagnosing automation failures
• Build and maintain test environments and test data as needed
• Mentor junior team members and contribute to QA standards and best practices
• Thrive in an environment where there is close interaction among different roles and teams
• Are admired by the client as someone they can count on, regardless of title or seniority

Requirements:

• 5+ years of professional testing web applications experience
• Experience with test frameworks, programming languages, and automation tools (Selenium, Jest, JUnit, CodeceptJS, JavaScript)
• Understanding of software development lifecycles, QA methodologies, and testing processes
• Experience testing APIs and working with relational/non-relational databases
• Experience working with cloud platforms (AWS, Azure, or GCP)
• Ability to debug issues and assist in root-cause analysis
• Strong sense of ownership, independence, and accountability
• Familiarity with CI/CD systems and modern release pipelines
• Strong communication skills and the ability to collaborate with cross-functional teams
• Bachelor’s degree in Computer Science, Software Engineering, MIS, or equivalent experience

Benefits of Being a Callibrity Consultant:

• As a consultant, you’re trusted as an advisor — clients look to you not just for implementation, but for guidance, strategic thinking, and leadership.
• Work alongside strong engineering teams and modern technology stacks
• Competitive Pay
• Impactful Work: Help drive application best practices in a respected organization
• Highly collaborative, low-ego environment focused on problem solving
• Meaningful and impactful client-facing work
• “Small company feel” backed by a stable, award-winning organization
• Company Events

Senior Data Scientist Consultant, SPI – Agent Platform Team

Summary

The Agent Platform Team, within AI Enablement at 84.51°, owns and manages the core platforms that power data science and AI across the organization. This role sits on the SPI (Science Platform Innovation) squad and is focused specifically on the Databricks environment and its evolving AI/ML capabilities.

You will be the bridge between the engineers building and maintaining our Databricks platform and the data scientists who use it every day. You are not building models for end-clients. Instead, you ensure the platform behaves as expected for the people on it—that new capabilities are tested, documented, and adopted smoothly, and that practitioner pain points make it back to the platform team in a way that shapes the roadmap.

You’ll build deep familiarity with Databricks’ infrastructure and AI powered capabilities —including Genie Code, Genie spaces, Agent Bricks, MLflow, and other features as they emerge—as well as the Unity Catalog governance layer that underpins them. You’ll test and evaluate new Databricks features and relevant third-party integrations from the perspective of an end-user, document your findings, and help the team decide what to adopt, how to roll it out, and what to skip.

This is a small, highly collaborative squad. Everyone pitches in on a bit of everything. Priorities shift frequently as the Databricks roadmap evolves. Success in this role is less about fixed technical expertise and more about comfort with ambiguity, good judgment about where to spend your time, and the ability to influence without direct authority.

Responsibilities

• Voice of the practitioner: Serve as the primary liaison between data scientists using Databricks and the platform engineering team. Gather requirements, surface pain points, and translate practitioner needs into actionable feedback that shapes platform decisions and roadmaps.
• Technology evaluation: Lead the testing and evaluation of new Databricks capabilities (e.g., Mosaic AI features, Model Serving enhancements, Genie, new Unity Catalog functionality) and relevant third-party tools. Document findings, recommended use cases, limitations, and rollout considerations. Over time, this may evolve from hands-on beta testing toward designing and coordinating evaluation efforts across groups of practitioners.
• Onboarding & enablement: Write guides, maintain documentation, lead training sessions, and run office hours to help data scientists adopt new platform capabilities. Partner with Learning & Development where appropriate. Serve as a first line of support when teams hit roadblocks.
• Adoption & communication: Evangelize Databricks capabilities to increase awareness and drive adoption. Produce clear release notes, communications, and materials for both technical and non-technical audiences.
• Cross-functional collaboration: Work closely with the solution architect, cloud engineer, and product senior on the squad to ensure proposed technologies have well-defined use cases and that guidelines and best practices are established. Act as a data science ambassador to cross-functional teams, explaining recommended workflows and the reasoning behind platform choices.
• Governance & compliance support: Help validate that new platform capabilities meet enterprise requirements around security, compliance, observability, and data governance (e.g., Unity Catalog lineage, Azure connectivity).

Qualifications & Experience

• Bachelor’s degree in mathematics, statistics, computer science, economics, or a related discipline.
• 2–4 years of experience in data science, AI/ML development, or applied analytics.
• 2–4 years of experience in tech consulting, retail analytics, or related professional services.
• Hands-on experience with Python, SQL, PySpark and Databricks
• Experience gathering user needs, documenting requirements, and driving issues to resolution with technical teams.
• Experience partnering with software engineering and/or product management teams.
• Familiarity with LLMs, prompt engineering, or agentic AI concepts is a strong plus.
• Experience with the Databricks ecosystem (Unity Catalog, MLflow) is a strong plus.
• Exposure to agent frameworks (e.g., LangChain, LangGraph, CrewAI, AutoGen) is a plus.

Skills

• Strong written and verbal communication, including the ability to explain technical concepts clearly to non-technical audiences and produce high-quality documentation.
• Ability to influence across teams and seniority levels without direct authority.
• Comfort operating in ambiguity—priorities shift frequently, and you need good instincts about where to focus.
• Strong project and time management skills; able to manage multiple priorities with minimal oversight.
• Curiosity and enthusiasm for emerging AI technologies and a genuine desire to become a subject matter expert on the Databricks platform and its AI capabilities.

#LI-SSS

Lead API Platform Engineer (Cincinnati / Chicago) P181

About 84.51°

84.51° is a data science and technology company (owned by Kroger) that turns customer data into decisions that improve how people shop. Engineers here work with modern cloud and data tech at meaningful scale, alongside product, data science, and business teams.

The role

We’re hiring a Lead API Platform Engineer to join our Application Delivery team within Platform Engineering. This is an individual contributor (IC) technical lead role, owning the API gateway experience end-to-end — from pre-production through production. You’ll help shape technical direction, mentor engineers, and continuously improve our on-call/escalation rotation — participating and fielding gateway-related production issues when on rotation.

If you meet most—but not all—requirements, we still encourage you to apply.

What you’ll do

• Own our gateway platforms: Maintain and evolve our Kong Enterprise API Gateway and Azure Application Gateway configurations, standards, and best practices—and help design what’s next.
• Strengthen API security: Implement and promote API security standards, including OAuth2, JWT, and SAML, along with access controls and encryption.
• Improve reliability and performance: Monitor, troubleshoot, and optimize using Datadog. Tune latency, caching behavior, and rate limiting to support high-performing APIs.
• Automate delivery: Build and maintain automation and CI/CD workflows (using GitHub Actions) for gateway deployments and policy/configuration updates.
• Enable great developer experience: Improve how teams build and test APIs by standardizing gateway onboarding, providing self-service patterns, and supporting reliable local development workflows (e.g., local gateway tooling, mock/test routes, and clear “run it locally” docs).
• Shape work for success: define and decompose scope into ready-to-build requirements (clear user stories and acceptance criteria), aligning on delivery standards, and proactively removing blockers to keep execution on track.
• Support production operations: Provide L2/L3 support, participate in an on-call rotation, lead root-cause analysis, and drive permanent fixes.
• Collaborate and document: Partner with development, QA, security, and architecture teams. Create clear documentation, SOPs, and onboarding materials to help teams ship confidently.

What we’re looking for

• Bachelor’s degree (Master’s degree desired) typically in Computer Science, Management Information Systems, Mathematics, Business Analytics or another technically strong program.
• 5+ years of hands-on experience operating API gateways in production environments (especially Kong)
• Strong API fundamentals (REST, JSON) and security best practices
• Experience working in a public cloud environment, plus solid networking knowledge (DNS, firewalls, load balancers) – Azure preferred but not required
• Comfort scripting and/or coding in one or more languages (e.g., Python, Shell, Java, Node.js)
• Familiarity with DevOps/Agile practices, Docker/Kubernetes/Helm, and Terraform (or other IaC tools)
• Clear communication skills and a collaborative mindset in a fast-moving environment

#LI-SSS

Sr. ML Data Engineer, Relevancy Sciences – Personalization & Loyalty Strategy (P508)

The Relevancy Sciences Team is responsible for creating relevant and personalized customer experiences for Kroger's E-commerce platform, which ranks among the top 10 ecommerce companies in the US. We generate trillions of recommendations at scale and deliver them to millions of Kroger customers daily. Our team maintains a comprehensive portfolio of machine learning solutions for search & product recommendations. We are seeking a talented and experienced Senior ML Data Engineer to join our data science team, with specialized expertise in building search and recommender systems.

Role Overview

You will architect, build, and operate the critical data infrastructure that powers our machine learning models, spanning from feature engineering to training data generation. This role serves as the bridge between ML requirements and production data systems, with ownership of feature stores, training/evaluation pipelines, and ML-specific data operations. You will enable data scientists to iterate rapidly while ensuring production-grade reliability and scalability.

What You'll Do

1. Feature Store Operations & Governance (40%)

• Own the feature request lifecycle from intake through deployment, driving reusability and maintaining a searchable feature catalog
• Design and build scalable feature pipelines that compute features from diverse sources (BigQuery, Azure Data Lake) and write to Feature Store infrastructure (Vertex AI Feature Store + BigQuery)
• Build streaming feature engineering pipelines using Apache Beam/Dataflow for real time feature computation and low-latency model serving with sub-second data freshness
• Ensure point-in-time correctness and online/offline feature consistency to prevent data leakage
• Implement drift detection, data quality monitoring, and alerting mechanisms
• Develop self-service tools and templates that enable teams to independently create features

1. Training & Evaluation Data Pipelines (30%)

• Build automated pipelines that generate ML-ready training datasets by combining features with labeled target variables
• Implement point-in-time correctness logic and sophisticated sampling strategies to ensure balanced, representative datasets
• Maintain comprehensive dataset versioning for full traceability across model versions
• Generate detailed evaluation reports with performance metrics segmented by business dimensions
• Support operations across both Azure and Vertex AI environments during platform migration

1. ML Data Operations & Reliability (20%)

• Serve as Tier 2/3 on-call responder for feature data quality incidents, diagnosing and resolving pipeline failures and performance issues
• Maintain comprehensive lineage tracking and metadata management for full data traceability
• Support regulatory compliance through proper data governance and documentation

1. Standards, Education & Collaboration (10%)

• Establish and enforce feature naming conventions, data quality thresholds, and point-in-time correctness patterns
• Conduct workshops on feature engineering best practices and provide expert guidance on feature design
• Partner with Data Scientists, ML Engineers, Data Engineering, and MLOps teams to optimize infrastructure and align with technical strategy

What We're Looking For

Required Qualifications:

• 3+ years of hands-on experience building and maintaining ML data pipelines in production environments with demonstrated expertise in scaling and reliability
• Expert-level SQL skills and advanced Python programming capabilities with experience in data processing frameworks and ML libraries
• Proven experience with cloud data platforms, with strong preference for GCP ecosystem including BigQuery, Dataflow, Vertex AI Feature Store, and associated ML services
• Deep understanding of end-to-end ML workflows including training data preparation, model evaluation methodologies, and serving infrastructure requirements
• Production operations mindset with experience in monitoring, alerting, on-call responsibilities, and meeting SLA commitments

Strongly Preferred Qualifications:

• Hands-on experience with Feature Store platforms such as Vertex AI Feature Store, Feast, Tecton, or similar enterprise solutions
• Deep knowledge of point-in-time correctness principles, temporal joins, and time-series data modeling best practices
• Multi-cloud experience with both Azure and GCP platforms, including data migration and hybrid cloud architectures
• Strong familiarity with core ML concepts including feature engineering, label creation, train/test/validation splits, and data leakage prevention
• Background spanning both analytics engineering and ML-specific data engineering with understanding of the unique requirements of each domain

Success Indicators:

• Improved Data Science Productivity:Data Scientists spend significantly less time on data preparation and infrastructure concerns, enabling more focus on model development and experimentation
• Increased Feature Reuse:Measurable increase in feature reuse across multiple models and teams, reducing redundant development effort and improving consistency
• Reliable Automation:Training and evaluation data generation processes operate reliably with minimal manual intervention and high uptime
• Efficient Incident Response:Data quality incidents are triaged quickly with clear escalation paths and rapid resolution times
• Accelerated ML Iteration:Overall ML model development and iteration velocity improves measurably across all teams using the platform

#LI-SSS

#LI-Remote