Data Product Manager

Job Title: Data Product Manager Location: Bismarck - North Dakota/ Boise Idaho/Burlington Iowa/Fargo - North Dakota/Grand Forks- North Dakota/Lewiston - Maine/Minot - North Dakota/ Portland - OR/ Renton - Washington/ Spokane Washington/ Salt Lake City UT/ Vancouver Washington+MN.

Duration

Contract

Job Description

Responsibilities: -

• Strong Data Analysis & Technical Discovery

+ Ability to independently trace and understand complex data flows end-to-end, from source systems and ingestion through Foundation/Enriched data products and downstream consumption.

+ Strong SQL skills with the ability to perform data profiling, reconciliation, root-cause analysis, and product validation.

+ Ability to investigate unfamiliar datasets and determine data meaning, relationships, business rules, quality issues, and downstream impacts.

+ Comfortable working through ambiguity and tracking down details across documentation, code, SMEs, data, and existing implementations.

+ Able to translate technical findings into clear business implications, requirements, and recommended next steps.

• Enterprise Data Product Design, Reusability & Integration

+ Experience designing enterprise data products for reuse across multiple consumers and use cases, rather than building narrowly tailored point solutions.

+ Ability to translate business needs into scalable data requirements, source-to-target mappings, transformation rules, acceptance criteria, and validation plans.

+ Understands data product design principles that promote flexibility, extensibility, discoverability, and reuse, allowing new populations, affiliations, channels, and use cases to be incorporated without unnecessary redesign.

+ Able to separate core reusable data and business logic from consumer-specific presentation or application logic, supporting multiple downstream experiences from common data assets.

+ Understands how to design data products with consistent definitions, keys, relationships, metadata, lineage, and business rules that make the data easier for both people and technology to consume.

+ Comfortable assessing how source-system, schema, population, or business-rule changes affect existing products and downstream consumers.

+ Experience with data migrations, modernization, or new source integrations where existing functionality must be preserved while the underlying architecture evolves.

+ Understands that modern data products may ultimately support BI/reporting, operational applications, semantic layers, AI assistants, and agents, and considers those consumption patterns during design.

• Technical Documentation & Requirements Excellence

+ Demonstrated ability to create clear, reference-quality technical documentation that can be used by engineering, product, QA, and downstream teams.

+ Strong experience developing and maintaining artifacts such as:

i. Source-to-target mappings

ii. Data requirements and field definitions

iii. Business and transformation rules

iv. Data lineage / flow documentation

v. Acceptance criteria

vi. Validation/reconciliation plans

• Ability to take fragmented information from multiple stakeholders and sources and turn it into a coherent, complete technical specification.
• Meticulous attention to detail and strong follow-through from discovery through implementation and production validation.
• Cross-Functional Collaboration & Ownership

+ Proven ability to work across Product, Engineering, Architecture, source-system teams, downstream consumers, and business stakeholders.

+ Strong facilitation skills with the confidence to ask detailed questions and challenge assumptions when requirements, mappings, or data behavior are unclear.

+ Proactively identifies dependencies, gaps, risks, and downstream impacts rather than waiting for them to surface during testing.

+ Able to manage multiple concurrent initiatives and maintain continuity as priorities shift.

+ Balances collaboration with accountability and raises concerns constructively when data quality or solution quality is at risk.

• Learning Agility, Emerging Technology & Knowledge Continuity

+ Demonstrated ability to quickly learn complex data domains, existing products, undocumented business rules, technical architectures, and new technologies.

+ Comfortable taking ownership of unfamiliar data products and progressively developing into a subject-matter resource.

+ Strong curiosity about evolving data architecture and consumption patterns, including semantic layers, AI-ready data products, enterprise knowledge models, and agentic workflows.

+ Able to understand how advances such as Snowflake Cortex, semantic models/layers, AI assistants, and autonomous or semi-autonomous agents change the way enterprise data needs to be structured, described, governed, and exposed.

+ Interested in learning how traditional data assets can evolve beyond tables and views into well-described, context-rich data products that can be reliably understood and consumed by both humans and AI.

+ Comfortable experimenting with new technologies while maintaining appropriate focus on data quality, governance, reliability, and business value.

+ Proactively captures institutional knowledge in durable artifacts rather than allowing critical business rules and technical knowledge to remain tribal.

+ Willing to cross-train across multiple data products and domains rather than becoming narrowly specialized in one initiative.

+ Able to learn from existing SMEs and convert that knowledge into repeatable processes, reusable patterns, and reference-quality documentation that other team members can use.

Mandatory Technical skills: -

• SQL & Data Analysis Advanced

+ Complex querying across multiple tables and data sources

+ Data profiling, reconciliation, troubleshooting, and root-cause analysis

+ Data validation and quality assessment

+ Ability to independently investigate unfamiliar datasets and relationships

+ Ability to compare source and target data and identify mapping, population, and transformation discrepancies

• Snowflake Working Knowledge to Intermediate

+ Experience navigating enterprise Snowflake environments

+ Working knowledge of databases, schemas, tables, views, roles, and data relationships

+ Ability to investigate data across multiple schemas and enterprise data products

+ Experience supporting data migration, integration, and modernization initiatives

+ Familiarity with Snowflake data sharing and modern Snowflake capabilities is beneficial

• Source-to-Target Mapping & Data Integration Advanced

+ Creation and interpretation of detailed source-to-target mappings

+ Definition of field-level transformations, business rules, keys, and relationships

+ Understanding of data lineage from source through ingestion, enrichment, and consumption

+ Experience working across batch, database, file, vendor/external, and event-driven data sources

+ Ability to assess upstream/downstream impacts of source, schema, population, or business-rule changes

• Enterprise Data Product Design Intermediate to Advanced

+ Experience designing reusable enterprise data products rather than point solutions

+ Ability to design data assets for flexibility, extensibility, and reuse across multiple consumers and use cases

+ Understanding of how to separate reusable enterprise data/business logic from consumer-specific logic

+ Experience defining reusable tables, views, data products, common business rules, and consumption patterns

+ Ability to anticipate new populations, affiliations, channels, and consumers without unnecessarily designing solutions around a single use case

+ Familiarity with dimensional modeling, semantic modeling, and other approaches to organizing data for consistent enterprise consumption

• Data Validation, Testing & Reconciliation Advanced

+ Development of data validation approaches, test scenarios, and acceptance criteria

+ Source-to-target reconciliation

+ Population and record-count validation

+ Identification of duplicates, missing populations, unexpected filtering, and transformation issues

+ Support of UAT, production validation, and troubleshooting

• Technical Documentation & Requirements Advanced

+ Creation of reference-quality source-to-target mappings, technical requirements, business rules, data dictionaries, lineage, and process documentation

+ Ability to transform fragmented information from SMEs, documentation, code, and data analysis into clear technical specifications

+ Strong documentation practices that reduce dependency on tribal knowledge

+ Confluence, SharePoint, Jira, Smartsheet, or comparable enterprise delivery/documentation tools

• Semantic Layers & AI-Ready Data Products Working Knowledge to Intermediate

+ Understanding of semantic layers/models and their role in making enterprise data easier to consistently discover and consume

+ Ability to think beyond traditional tables and views to how data definitions, relationships, metadata, and business context can enable AI-assisted consumption

+ Awareness of how reusable enterprise data products can support analytics, applications, natural-language querying, AI assistants, and agents

+ Interest in developing deeper expertise as enterprise data consumption evolves

• Event-Driven Data & APIs Working Knowledge

+ Familiarity with event-driven data architectures and ingestion patterns

+ Experience working with JSON or comparable semi-structured data

+ Understanding of APIs, events, and event-streaming concepts

+ Kafka, ELS, or comparable event technology experience is beneficial

• Python Nice to Have / Working Knowledge

+ Ability to use Python for data profiling, reconciliation, automation, or analysis is beneficial

+ Advanced Python development is not required if the candidate demonstrates strong SQL, data-analysis, and technical-discovery capabilities