Overview
On Site
Contract - W2
Skills
Financial Forecast
Economics
Sales
Pivotal
Product Requirements
Embedded Systems
Regulatory Compliance
Data Analysis
Functional Requirements
Predictive Modelling
Forecasting
Advanced Analytics
Decision-making
Data Governance
Meta-data Management
Access Control
Data Validation
Documentation
Mentorship
Design Review
Semantics
Use Cases
Specification Gathering
Workflow
Analytical Skill
Artificial Intelligence
Collaboration
Communication
Facilitation
Machine Learning (ML)
Mapping
Business Process
Data Structure
SQL
Data Profiling
Informatica
Data Quality
Data Processing
Snow Flake Schema
Databricks
Amazon Web Services
Analytics
User Stories
Data Modeling
DICE
Job Details
As part of the transformation, we are building a modern, governed, and reusable data foundation to power financial forecasting, title economics, content sales planning, and AI-driven insights across the enterprise.
The Senior Data Analyst plays a pivotal role in shaping that foundation-translating product requirements into robust, scalable data models that serve both immediate application needs and long-term analytical and AI objectives.
Embedded within the Platform Pod, this role works closely with Application Designers, Platform Engineers, the Senior Data Architect, and product-aligned pods to ensure application-specific data models integrate seamlessly with the enterprise data platform. They act as the primary bridge between feature-level requirements and platform-level data strategy-ensuring reusability, governance compliance, and analytical readiness.
Job Responsibilities / Typical Day in the Role
Lead Data Analysis for Application & Platform Alignment
Translate product features and user stories into well-defined data model requirements that support application workflows and downstream analytics.
Partner with Application Designers and Engineers to profile, assess, and validate source data, ensuring it meets both functional and non-functional requirements.
Collaborate with the Senior Data Architect to align application data models with canonical and semantic models across domains.
Ensure Long-Term Analytical & AI Enablement
Design data structures and pipelines that serve both operational application needs and future analytical/AI use cases.
Anticipate and define data capture, transformation, and enrichment requirements to support predictive modeling, forecasting, and advanced analytics.
Recommend optimizations that improve data quality, timeliness, and completeness for decision-making.
Governance, Quality, and Documentation
Partner with enterprise data governance teams to apply metadata, lineage, and access control standards.
Define and execute data validation, profiling, and reconciliation processes to ensure trusted results.
Maintain documentation of data definitions, mapping specifications, and lineage diagrams for both applications and analytical datasets.
Cross-Pod Collaboration and Mentorship
Lead cross-pod workshops to resolve semantic conflicts, promote reusable data assets, and ensure consistent application of standards.
Mentor junior analysts and support teams in data discovery, mapping, and quality assessment best practices.
Represent the Platform Pod's data perspective in architecture boards, product councils, and design reviews.
Must Have Skills / Requirements
1) Proven expertise in data modeling techniques (relational, dimensional, wide-table for ML, data vault) and mapping business processes to data structures.
a. 7-10+ years of experience
2) Strong proficiency in SQL, data profiling, and transformation tools (e.g., dbt, Informatica, AWS Glue).
a. 7-10+ years of experience
3) Familiarity with distributed data processing and analytics platforms (e.g., Snowflake, Databricks, AWS-native analytics stack).
a. 7-10+ years of experience
4) Experience Translating product features and user stories into well-defined data model requirements
a. 7-10+ years of experience
Functional Knowledge / Skills in the following areas:
1) You'll thrive in this role if you:
a. Think in Domains and Products - You design data solutions that reflect business semantics and scale across use cases.
b. Bridge Technical and Business Worlds - You can translate analytical needs into technical specifications and vice versa.
c. Govern Through Enablement - You make governance easy to adopt by embedding it directly into design and workflows.
d. See Beyond the Immediate - You anticipate future analytical and AI needs when designing today's application data structures.
e. Collaborate to Elevate - You work across functions to raise the quality, reusability, and reliability of data.
2) What You'll Bring:
a. Excellent communication and facilitation skills to influence design decisions across product, platform, and governance teams.
Technology Requirements:
1) Proven expertise in data modeling techniques (relational, dimensional, wide-table for ML, data vault) and mapping business processes to data structures.
2) Strong proficiency in SQL, data profiling, and transformation tools (e.g., dbt, Informatica, AWS Glue).
3) Experience in data quality frameworks, validation automation, and reconciliation methods.
4) Familiarity with distributed data processing and analytics platforms (e.g., Snowflake, Databricks, AWS-native analytics stack).
5) Demonstrated ability to align cross-team requirements into unified, reusable, and governed data solutions.
6) Experience Translating product features and user stories into well-defined data model requirements
Additional Notes
Hybrid schedule - 3 days on-site (Burbank, CA)
#LI-NN2
#LI-hybrid
#DICE
The Senior Data Analyst plays a pivotal role in shaping that foundation-translating product requirements into robust, scalable data models that serve both immediate application needs and long-term analytical and AI objectives.
Embedded within the Platform Pod, this role works closely with Application Designers, Platform Engineers, the Senior Data Architect, and product-aligned pods to ensure application-specific data models integrate seamlessly with the enterprise data platform. They act as the primary bridge between feature-level requirements and platform-level data strategy-ensuring reusability, governance compliance, and analytical readiness.
Job Responsibilities / Typical Day in the Role
Lead Data Analysis for Application & Platform Alignment
Translate product features and user stories into well-defined data model requirements that support application workflows and downstream analytics.
Partner with Application Designers and Engineers to profile, assess, and validate source data, ensuring it meets both functional and non-functional requirements.
Collaborate with the Senior Data Architect to align application data models with canonical and semantic models across domains.
Ensure Long-Term Analytical & AI Enablement
Design data structures and pipelines that serve both operational application needs and future analytical/AI use cases.
Anticipate and define data capture, transformation, and enrichment requirements to support predictive modeling, forecasting, and advanced analytics.
Recommend optimizations that improve data quality, timeliness, and completeness for decision-making.
Governance, Quality, and Documentation
Partner with enterprise data governance teams to apply metadata, lineage, and access control standards.
Define and execute data validation, profiling, and reconciliation processes to ensure trusted results.
Maintain documentation of data definitions, mapping specifications, and lineage diagrams for both applications and analytical datasets.
Cross-Pod Collaboration and Mentorship
Lead cross-pod workshops to resolve semantic conflicts, promote reusable data assets, and ensure consistent application of standards.
Mentor junior analysts and support teams in data discovery, mapping, and quality assessment best practices.
Represent the Platform Pod's data perspective in architecture boards, product councils, and design reviews.
Must Have Skills / Requirements
1) Proven expertise in data modeling techniques (relational, dimensional, wide-table for ML, data vault) and mapping business processes to data structures.
a. 7-10+ years of experience
2) Strong proficiency in SQL, data profiling, and transformation tools (e.g., dbt, Informatica, AWS Glue).
a. 7-10+ years of experience
3) Familiarity with distributed data processing and analytics platforms (e.g., Snowflake, Databricks, AWS-native analytics stack).
a. 7-10+ years of experience
4) Experience Translating product features and user stories into well-defined data model requirements
a. 7-10+ years of experience
Functional Knowledge / Skills in the following areas:
1) You'll thrive in this role if you:
a. Think in Domains and Products - You design data solutions that reflect business semantics and scale across use cases.
b. Bridge Technical and Business Worlds - You can translate analytical needs into technical specifications and vice versa.
c. Govern Through Enablement - You make governance easy to adopt by embedding it directly into design and workflows.
d. See Beyond the Immediate - You anticipate future analytical and AI needs when designing today's application data structures.
e. Collaborate to Elevate - You work across functions to raise the quality, reusability, and reliability of data.
2) What You'll Bring:
a. Excellent communication and facilitation skills to influence design decisions across product, platform, and governance teams.
Technology Requirements:
1) Proven expertise in data modeling techniques (relational, dimensional, wide-table for ML, data vault) and mapping business processes to data structures.
2) Strong proficiency in SQL, data profiling, and transformation tools (e.g., dbt, Informatica, AWS Glue).
3) Experience in data quality frameworks, validation automation, and reconciliation methods.
4) Familiarity with distributed data processing and analytics platforms (e.g., Snowflake, Databricks, AWS-native analytics stack).
5) Demonstrated ability to align cross-team requirements into unified, reusable, and governed data solutions.
6) Experience Translating product features and user stories into well-defined data model requirements
Additional Notes
Hybrid schedule - 3 days on-site (Burbank, CA)
#LI-NN2
#LI-hybrid
#DICE
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.