Main Responsibilities and Required Skills for Data Modeler
A Data Modeler is responsible for developing and maintaining conceptual, logical and physical data models. They analyze and understand complex databases and govern data quality and data flows. In this blog post we describe the primary responsibilities and the most in-demand hard and soft skills for Data Modelers.
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Main Responsibilities of Data Modeler
The following list describes the typical responsibilities of a Data Modeler:
Acquire
Acquire data from primary or secondary data sources and maintain databases / data systems.
Advise
Advise and implement best practice data modelling production, storage, and consumption standards.
Advise clients on optimal BI / analytics strategy and its execution.
Align
Align hierarchy with corresponding business rules, requirements, and data transformations.
Analyze
Analyze all gathered requirements.
Analyze and understand complex databases.
Analyze / Clean / Load / Stage payer data from disparate sources into centralized data marts.
Analyze data and information requirements.
Analyze data-related system integration challenges and propose appropriate solutions.
Analyze existing database designs for performance or feature enhancement.
Analyze structural requirements for new software and applications.
Articulate
Articulate data modelling principles to other modelers and business users.
Assist in / Assist with
Assist business analysts to clarify requirements' ambiguities.
Assist in defining modeling patterns, templates and best practices.
Assist in development and maintaining our Company's Drug Discovery Reference Model.
Assist in troubleshooting application problems where data, ETL, and reporting are integral elements.
Assist with developing modeling to support enhancements and business needs.
Assist with the collection and validation of business requirements.
Automate
Automate the process of campaign reporting to improve efficiency.
Build
Build and maintain strong relationships with our key architects and stakeholders.
Capture
Capture data models from existing databases and record descriptive information into ERWin.
Champion
Champion creativity and innovation.
Collaborate with
Collaborate cross functionally within the team and participate in projects outside the team.
Collaborate well with distributed team structure.
Collaborate with IT Architecture, Product Owners and team to devise data model to suit requirements.
Collaborate with management and internal teams to implement and evaluate improvements.
Collaborate with other IT teams, Architecture and third party vendors as required.
Collaborate with other teams and departments to ensure excellent levels of client service.
Collaborate with third party vendors for projects.
Communicate
Communicate clearly and concisely, both verbal and written.
Communicate effectively with no ambiguity during project execution on data model.
Communicate project status(s) within IT.
Communicate with internal and external stakeholders and manage their expectations.
Complete
Complete Data Architecture portion of the Solution Architecture Document.
Conduct
Conduct and support data analysis, data mining, and identify patterns.
Conduct basic project leadership.
Contribute to
Contribute to database tuning and performance enhancement at a server and database level.
Coordinate
Coordinate and guide offshore DEV team members.
Coordinate with the Integration department to identify future needs and requirements.
Create
Create a data dictionary of terms that normalizes data from multiple sources.
Create and follow timelines.
Create and Maintain Conceptual, Logical and Physical Data Models.
Create and maintain configuration notes for the testing environments.
Create and maintain Logical Data Model (LDM) and Physical Data Model (PDM) for the project.
Create and maintain the Source to Target Data Mapping document for this project.
Create and populate Data Dictionary to support and define data elements in data model.
Create conceptual data model to identify key business entities and visualize their relationships.
Create / maintain enterprise data model and data dictionary.
Create technical documentation to support all of the above.
Curate
Curate legacy scientific data according to defined data standards.
Define
Define and manage an exception process to manage hierarchies and cross functional metrics.
Define process to analyze and map specific data hierarchies across functions, customers, and systems.
Define security and backup procedures.
Define sources and calculations for metrics and KPIs.
Define the entity based logic model and all elements pertaining to each Entity.
Define the scope and schedule of User Acceptance test phases.
Delve into
Delve into data to discover discrepancies and patterns.
Design
Design conceptual and logical data models and flowcharts.
Design conceptual and logical data models on Morningstar contents.
Design Logical Data Model and Physical Data Model.
Design star / snowflake schemas.
Design Tables spreadsheets for developer to use.
Develop
Develop and implement solution architecture.
Develop and implement standards and processes for ensuring data quality.
Develop and maintain conceptual, logical and physical data models.
Develop and maintain data models.
Develop and maintain risk adjustment calculation engines using appropriate data sources and software.
Develop database solutions to store and retrieve centralized customer information.
Develop data dictionaries, aligning to our Company's existing vocabularies & ontologies.
Develop data integration and ETL solutions between our various databases and business systems.
Develop data mapping documents for data development teams to use.
Develop data models according to company standards.
Develop data models and mappings for database, data lakes and data warehouses.
Develop DDL implementation / back-out plans.
Develop logical and physical data models for Traditional Data Stores and NoSQL.
Develop own expertise and share it with the team members to help grow the team.
Develop project plans and provide time estimates on related project work.
Develop project-related data models and align to our Company's drug discovery reference model.
Develop test procedures and methodologies to validate designs and implementations.
Develop tight-knit collaboration between globally distributed team members.
Document
Document and implement test strategies, plans, processes and standards.
Document data flows, identify appropriate data sources, entities, relationships and redundancies.
Document design and build book for Data Models and Data Architecture.
Draft
Draft Design and Develop Entity Relationship Data Model for IAM Domain.
Drive
Drive awareness and adoption of a data servicing and sharing culture.
Elicit
Elicit subject matter expert expectations on the behavior of the system.
Ensure
Ensure a consistent approach and implementation of processes and technology in designing solutions.
Ensure adherence to standards across data product initiatives.
Ensure CMS data and data management standards are met and maintained.
Ensure compliance with Enterprise Architecture, Technology Standards, and Security.
Ensure data objects adhere to defined Freddie Mac standards and best practices.
Establish
Establish Data Architecture based on gathered requirements.
Establish Key Performance Indicators (KPIs) that will best show the success of each campaign.
Establish, modify, and maintain a conceptual data model.
Evaluate
Evaluate data models and physical databases for variances and discrepancies.
Examine
Examine new application design and recommend corrections if required.
Follow
Follow up, track, and document in support of the engineering team needs.
Gather
Gather business requirements from key project stakeholders.
Gather technical requirements from Support teams.
Generate
Generate DDL and validate that the DDL is implemented properly.
Generate DDL script from data modelling tool and to ensure the script's proper function.
Generate metadata for Hadoop ingestion for different frameworks.
Govern
Govern data quality and data flows in the Enterprise.
Guide
Guide team members with data normalization processes.
Guide the data mapping efforts between SESIS and other existing data sources.
Help
Help build a data catalogue, metrics store, and various data products.
Help create data structures and adapt them in application and API contracts.
Help development team to optimize database performance.
Help out data integration / migration in-between systems.
Identify
Identify and confirm participants.
Identify common data elements that need to be setup for use by multiple stakeholders.
Identify, develop and analyze key performance indicators.
Identify gaps between the current deployment of applications and future requirements.
Identify opportunities to reuse data and reduce redundancy in data across the enterprise.
Implement
Implement appropriate data modelling governance framework.
Implement governance practices - implementing controls / governance across all CMS data domains.
Improve
Improve system performance by conducting tests, troubleshooting and integrating new elements.
Interpret
Interpret CMS Advance Notices, and Federal Register documentation.
Interpret data transformation and filtering rules to create data maps for ETL developers to use.
Investigate
Investigate test outcome and raise requirements' ambiguities and defects using established processes.
Lead
Lead all aspects of dataset design, creation, and curation.
Lead metrics standardization in functions, reconciling data as necessary.
Lead solution design discussions with internal and external stakeholders as relevant.
Lead the effort to implement core reusable components.
Maintain
Maintain a "hands-on” attitude.
Maintain and support updates to Data Models and Data Architecture, throughout project lifecycle.
Maintain appropriate Version Control on data models.
Maintain conceptual, logical and physical data models along with corresponding metadata.
Maintain data dictionary and / or metadata repositories.
Maintain technical and business metadata.
Maintain the glossary / data dictionary.
Manage
Manage and maintain a centralized Data Models Repository.
Manage and oversee small- to medium-scaled projects conducted by the team when needed.
Manage data integrity and data quality activities.
Manage logical data model versioning and integration and prepare data models for deployment.
Manage several aspects of the engineering milestone process needs.
Mentor
Mentor and coach portfolio Data Modeler.
Mentor, contribute and govern Data Management standards and best practices.
Monitor
Monitor data quality and identify data anomalies and assess possible business impact.
Optimize
Optimize and update logical and physical data models to support new and existing projects.
Optimize new and current database systems.
Oversee
Oversee team area, keeping it organized and in order.
Own
Own and handle both the analytical data model and customer data model for Salesforce Marketing.
Own the data definitions for all data tables and maintain data lineage.
Participate in
Participate in application and API design, test, and implementation phases.
Participate in Data Governance procedures and policy management.
Participate in defining data architecture document standard.
Participate in ETL design, strategy and associated processes.
Perform
Perform a variety of complicated tasks.
Perform complex analysis of data emanating from the SESIS application.
Perform non‐functional testing resulting in usability data.
Perform reverse engineering of physical data models from databases and SQL scripts.
Prepare
Prepare data model documentation.
Prepare data model & solution design documentation.
Prepare material for and assist staff with user acceptance test sessions.
Prioritize
Prioritize system defect fixes and enhancements.
Promote
Promote data models into production metadata environments.
Propose
Propose technical designs and lead the implementation of data solutions.
Provide
Provide assistance on vendor analysis, selection and review when necessary.
Provide creative copy suggestions and advert ideas.
Provide data architecture for mobile initiatives and solutions.
Provide database operational considerations back to development teams.
Provide data management expertise to one or more program teams.
Provide key inputs to project lead and program management regarding progress, risks, dependencies.
Provide leadership and guidance with enterprise data strategies.
Provide operational support for DBA teams.
Provide source and destination mappings and business transformations for ETL Specification / Design.
Provide technical and data leadership to the application development group, IT, and the enterprise.
Raise
Raise and monitor requirements' ambiguities.
Recommend
Recommend opportunities for reuse of data models in new environments.
Reserve
Reserve resources for conducting sessions.
Resolve
Resolve functional and technical issues related to GCP business applications.
Retrieve
Retrieve data from legacy systems to new solutions.
Review
Review data requirements and analyse source systems data.
Review, install and configure information systems to ensure functionality and security.
Run
Run on-going A / B testing on different activity parameters.
Stay current on
Stay current on assessments of technology for the business.
Support
Support Agile squads with creation of DDL and table creation.
Support and provide guidance to ETL Developer.
Support and resolve metadata and data domain issues and queries raised by internal customers.
Support DDL PROD implementation.
Support more junior team members in day to day work.
Support post-production validation of DDL.
Translate
Translate business requirements into models, and into database scheme designs.
Translate business requirements into system definitions and solutions.
Translate business requirements into working logical and physical Service Layer Data models.
Troubleshoot
Troubleshoot issues with database views and query performance.
Understand
Understand and address data security risks.
Understand and translate business needs into data models supporting long-term solutions.
Understand business requirements and translate them to the enterprise data and process models.
Utilize
Utilize HPQC for raising and prioritizing defects.
Validate
Validate and comply with the defined data architecture.
Validate business data objects for accuracy and completeness.
Verify
Verify the system's conformance to defined user stories, functional requirements.
Work with
Work as a subject matter expert on high priority initiatives.
Work in an Agile Scrum methodology for evolving data model every sprint and deliver incrementally.
Work will primarily focus on creating & developing data models - conceptual, logical and physical.
Work with DBAs on imposing database constraints, indexes, performance tuning for the data models.
Work with direct report to create a way to visualize the data for easy interpretation.
Work with multiple data sources to compile, model and use in Tableau Dashboards and BI reporting.
Work with other physical data modelers to ensure proper lineage of contents in data models.
Write
Write queries for data from the Enterprise Data Hub for analysis.
Write queries to extract data from the Enterprise Data Hub, data warehouse / marts for analysis.
Most In-demand Hard Skills
The following list describes the most required technical skills of a Data Modeler:
Design
Data Analysis
Data Warehouse
Azure
Dimensional
Hadoop
Azure Data Factory
Data Architecture
PL / SQL
Most In-demand Soft Skills
The following list describes the most required soft skills of a Data Modeler:
Written and oral communication skills
Analytical ability
Problem-solving attitude
Organizational capacity
Team player
Detail-oriented
Influencing
Presentation
Work independently with little direction
Communicate complex technical concepts to non-technical people
Decision-making
Troubleshooting skills
