Data Product Manager

Duration: Contract

Job Description

Responsibilities: -

1. Strong Data Analysis & Technical Discovery
   1. 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.
   2. Strong SQL skills with the ability to perform data profiling, reconciliation, root-cause analysis, and product validation.
   3. Ability to investigate unfamiliar datasets and determine data meaning, relationships, business rules, quality issues, and downstream impacts.
   4. Comfortable working through ambiguity and tracking down details across documentation, code, SMEs, data, and existing implementations.
   5. Able to translate technical findings into clear business implications, requirements, and recommended next steps.

2. Enterprise Data Product Design, Reusability & Integration
   1. Experience designing enterprise data products for reuse across multiple consumers and use cases, rather than building narrowly tailored point solutions.
   2. Ability to translate business needs into scalable data requirements, source-to-target mappings, transformation rules, acceptance criteria, and validation plans.
   3. 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.
   4. Able to separate core reusable data and business logic from consumer-specific presentation or application logic, supporting multiple downstream experiences from common data assets.
   5. 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.
   6. Comfortable assessing how source-system, schema, population, or business-rule changes affect existing products and downstream consumers.
   7. Experience with data migrations, modernization, or new source integrations where existing functionality must be preserved while the underlying architecture evolves.
   8. 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.
3. Technical Documentation & Requirements Excellence
   1. Demonstrated ability to create clear, reference-quality technical documentation that can be used by engineering, product, QA, and downstream teams.
   2. 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

1. Ability to take fragmented information from multiple stakeholders and sources and turn it into a coherent, complete technical specification.
2. Meticulous attention to detail and strong follow-through from discovery through implementation and production validation.

2. Cross-Functional Collaboration & Ownership
   1. Proven ability to work across Product, Engineering, Architecture, source-system teams, downstream consumers, and business stakeholders.
   2. Strong facilitation skills with the confidence to ask detailed questions and challenge assumptions when requirements, mappings, or data behavior are unclear.
   3. Proactively identifies dependencies, gaps, risks, and downstream impacts rather than waiting for them to surface during testing.
   4. Able to manage multiple concurrent initiatives and maintain continuity as priorities shift.
   5. Balances collaboration with accountability and raises concerns constructively when data quality or solution quality is at risk.

3. Learning Agility, Emerging Technology & Knowledge Continuity
   1. Demonstrated ability to quickly learn complex data domains, existing products, undocumented business rules, technical architectures, and new technologies.
   2. Comfortable taking ownership of unfamiliar data products and progressively developing into a subject-matter resource.
   3. Strong curiosity about evolving data architecture and consumption patterns, including semantic layers, AI-ready data products, enterprise knowledge models, and agentic workflows.
   4. 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.
   5. 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.
   6. Comfortable experimenting with new technologies while maintaining appropriate focus on data quality, governance, reliability, and business value.
   7. Proactively captures institutional knowledge in durable artifacts rather than allowing critical business rules and technical knowledge to remain tribal.
   8. Willing to cross-train across multiple data products and domains rather than becoming narrowly specialized in one initiative.
   9. 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: -

1. 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
2. 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
3. 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
4. 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
5. 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

6. 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
7. 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
8. 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
9. 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