About the roleEnterprises are increasingly looking to modernize their data ecosystems, improve the reliability and accessibility of their data, and prepare for AI-driven solutions. Often, the biggest challenges aren't the applications themselves, but the underlying data architecture, integration, and quality needed to support them. Every client environment is different, requiring someone who can quickly diagnose complex data challenges and architect practical solutions that deliver measurable business value.
You will shape how enterprise data becomes usable for analytics, AI-assisted experiences, workflows, and other use cases across client organizations. You'll architect and help build scalable data solutions while defining practical standards and patterns that make client data easier to find, understand, use, and govern. You'll collaborate with client data owners and engineering teams to deliver prototypes and production-ready capabilities, demonstrating progress through measurable outcomes and clear executive communication. Beyond delivering on individual client engagements, you'll help establish reusable data architecture, engineering, and AI-readiness frameworks that support the growth of this emerging capability at Phase2.
What You'll DoClient Leadership- Actively help pitch and sell this capability alongside sales and account teams
- Lead technical client conversations, diagnosing underlying data challenges and translating business needs into practical solutions rather than simply executing what's requested
- Make the business case for foundational data work independent of any single AI use case, including quantifying cost avoidance, reporting-time savings, and eliminated duplicate work so clients see the value even before, or without, an AI initiative
- Establish measurable outcomes and progress indicators that demonstrate the value of technical improvements, and communicate results through regular demos and executive updates
Strategy and Architecture- Collaborate with Phase2's senior technical leaders to establish reusable architectures, technical standards, and delivery approaches that help develop and scale our emerging enterprise data capabilities
- Partner with the Marketing Technology Strategists and Data & Insights team to align semantic standards and warehouse architecture with domain-specific marketing data work (CDP, attribution, activation)
- Establish standards for semantic and context layers to improve consistency, interpretability, and reuse across a client's data ecosystem
- Pinpoint and resolve semantic and contextual gaps that limit data consistency, accessibility, and reliability, collaborating with client data owners to establish trusted, reusable data foundations across systems, teams, and AI applications
- Design and deliver metadata, lineage, and data quality practices that improve discoverability and reduce ambiguity in AI-driven analysis
- Define scalable data access and integration patterns that support analytics, AI applications, copilots, and agents
Implementation and Delivery- Architect, build, and optimize data platforms and pipelines, including data ingestion, integration, transformation, modeling, and storage, with attention to scalability, performance, reliability, and cost
- Design and deliver proofs of concept and production-ready data solutions across diverse client environments, from targeted analytics migrations to complex, large-scale data processing and AI-enabled applications
- Collaborate with software engineers and other technical specialists to implement solutions, establish reusable engineering patterns, and leverage AI-assisted development tools where appropriate
Quality and Governance- Establish data quality, validation, and testing practices that ensure solutions are reliable, accurate, and fit for their intended purpose, including AI use cases where applicable
- Define technical standards and review the quality of data solutions developed by engineers and AI-assisted tooling, ensuring consistency, maintainability, and correctness
- Incorporate appropriate documentation, metadata, lineage, and governance practices into solutions to support security, compliance, auditability, and responsible data use
What You'll Need- 8+ years of experience in data engineering, data architecture, or a closely related discipline (e.g., analytics engineering, data platform engineering, or MDM architecture)
- Demonstrated ability to establish and apply data quality, testing, and validation practices that ensure the accuracy and reliability of data solutions
- Ability to prototype and build solutions across the data and application stack to accelerate learning and delivery
- Demonstrated experience architecting and delivering production data platforms and solutions, including data integration, transformation, modeling, and storage, with sound judgment around scalability, performance, reliability, and cost
- Fluency in SQL and data modeling, with strong judgment around modeling approaches (e.g., Kimball, Data Vault) and when to apply them
- A demonstrated habit of tracing a client complaint (a wrong report, an unreliable AI answer, a stalled migration) back to its actual data root cause
- Experience navigating complex stakeholder environments, balancing technical, business, security, and compliance considerations to build alignment around solutions
- Strong executive communication and storytelling skills, including delivering progress against the scorecards/KPIs you define and influencing roadmaps
- Comfortable delivering visible progress in short cycles, showing a client tangible movement every week or two rather than asking them to wait months for a finished build
- A generalist orientation and comfort with ambiguity; ability to own a client conversation on data migration one week and a large-scale pipeline the next
What Would Be Nice to Have- Experience supporting regulatory audits or compliance reviews in regulated industries (financial services, healthcare, or similar)
- Experience bridging data product management and AI solution delivery
- Experience in metadata management and data catalog ecosystems, including improving discoverability and interpretability
- Full-stack engineering experience to support rapid prototyping and experimentation
- Experience building or governing controls and metrics layers for enterprise analytics at scale
- Experience directing AI agents/agentic tooling as a delivery mechanism, rather than only using them as personal productivity tools
What You'll Get- The chance to make a real impact, helping millions of people per year.
- To work from wherever you thrive best. We're a remote-first team built on trust, flexibility, and outcomes.
- A competitive compensation package that considers various factors.
- A comprehensive benefits package that includes medical, dental, vision, life insurance, disability coverage, and supplemental insurance.
- Our fun and interactive wellness program offers rewards for participation.
- A 401(k) with a 3% employer contribution-no match required and 100% vested after just 90 days.
- You'll start with 16 paid vacation days, 4 personal days, and 1 volunteer day each year. Vacation and personal time increase with your tenure, recognizing your continued contributions.
- Up to 12 weeks of fully paid leave for birthing parents and up to 6 weeks of fully paid bonding leave, based on your tenure. This includes support for adoption and foster placement, along with the option to extend time off through additional unpaid leave.
- A $500 stipend to personalize and equip your home workspace for success.
What the Environment Looks LikePhysical Demands- Frequently remain in a stationary position, often standing or sitting for prolonged periods of time.
- Occasional adjusting or moving of light objects up to 20 pounds in all directions.
- Continuous communication on live video chats, calls, and through emails and messaging platforms for the purpose of exchanging information.
- Continuous repetitive movements that may include the wrists, hands, and/or fingers.
- Travel, sometimes frequent, is expected.
Mental Demands- Comprehension - Must continuously be able to understand direction and adhere to established procedures. Must also be able to gather information from clients and employees and synthesize this information in a meaningful way. May also be responsible for making improvements or recommendations to existing procedures.
- Organization - Must continuously be able to gather and classify information, store information learned from conversations and communications, and then be and be able to quickly recall, interpret, and explain information.
- Reasoning and Decision Making - Must continuously use logic to analyze and interpret information, and prioritize.
- Communication - Must continuously express or exchange ideas and meaning with employees and clients.
- Resilience - Must continuously adapt well in the face of workplace stressors.
The pay range for this role is:
132,000 - 155,000 USD per year (Remote (United States))
