Head of Product and Technical Architecture

Technical Product Manager
Interview Mode: Interviews will be conducted via Microsoft Teams.

The work is expected to be done remotely or hybrid at DCYF's offices at 444 Lafayette Rd N, St. Project Schedule
Anticipated Project Start Date: September 2026
Define the vision and roadmap for data products (e.g., data platforms, analytics tools, ML infrastructure).
Identify high value opportunities by investigating the data landscape, pain points, and business needs.
Align data product strategy with organizational priorities and long-term data architecture, in partnership with the Enterprise Architecture team and various interested parties.
Connect data capabilities to business outcomes and organize efforts to achieve the business outcomes.
Data Product Development :

Lead the end-to-end lifecycle of data products: requirements, design, development, testing, launch, and iteration.
Partner with data engineers and data scientists to build scalable pipelines, models, and data services. Ensure data quality, governance, lineage, and documentation standards are met.
Translate business logic into data transformations, metadata, and domain specific rules. Skilled in or adept at data architecture, modeling, and pipelines.
Ensures data products are reliable, governed, and scalable.

Interested Parties Management :

Serve as the primary liaison between technical teams at Minnesota IT Services (MNIT) and business partners across DCYF.
Define success metrics and measure product performance and adoption.
Ensure data products deliver actionable insights and support decision making.
Governance, Compliance & Ethical Data Use :

Uphold data governance, privacy, and ethical AI standards.
Ensure compliance with regulatory and organizational data policies.
Advocate for responsible data use across the human services space served by and supported through DCYF and MNIT DCYF.

Desired 4 7 years of experience in Data management, data analytics, data engineering, or related fields.
Strong understanding of data systems: pipelines, warehousing, modeling, metadata, governance.
Proficiency collaborating with data Architecture, data engineering and data science teams.
Ability to translate complex technical concepts into business-friendly language.
Strong communication, prioritization, and stakeholder management skills.