Job Role:
Data Analyst
Responsible for the design of logical models based upon analysis of complex business requirements. Works
closely with customers to understand their business processes and data needs. Coordinates all workgroup
modeling activities. Reviews all models for completeness and standardization. Works under limited
supervision. Typically reports to a manager.
Responsibilities
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Function: Analyze and Design Database
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Activity: Adapt conceptual and logical data models to enterprise model
 Company data and objects standards policies are thoroughly followed
 Conceptual and logical data models are in line with enterprise model
 Possible adaptations of enterprise model are considered
Activity: Create, refine and distribute conceptual and logical data models
 All approvals are obtained for conceptual and logical data models
 Clients are consulted to determine attribute domains
 Conceptual model is documented accurately and completely
 Entities, attributes and relationships are identified and defined
 Key attributes are identified
Activity: Determine target environment and/or platform
 Available options are researched, analyzed and documented
 Performance requirements are researched and documented
 Platforms, environments and hardware are reviewed, and options and
recommendations are effectively communicated to appropriate personnel
 Target environment, performance platform and hardware are agreed upon
by key people and database technology is properly selected
Activity: Identify access and concurrency requirements
 Access plan is integrated with backup and recovery plan
 Select record locking mechanism based on data integrity goals
 Requirements are specific to database and operating system
Activity: Identify backup and recovery requirements and create recovery plan
 Backup and recovery requirements are consistent with corporate policy,
business needs and legal requirements
 Requirements are specific to database and are documented completely
Activity: Identify high-level business rules for data model
 Data constraints are identified and documented
 Data definitions are fully developed and agreed upon
 Entity level data ownership is clearly defined
 Detailed business data rules are documented
 High-level business rules are integrated within the data model
 Pertinent business rules are identified or defined during modeling
 Validation rules are identified and documented
Activity: Perform research and analyze requirements
 All approvals are obtained for data analysis and design document
 Business objectives and goals for the project are well defined
 Client/users are properly educated regarding technology and tools
 Complete data requirements are communicated and approved by client
 Scope and boundaries of data model are free of conflicts
 Sources of information are reliable and current
Activity: Validate conceptual and logical data models with clients
 Conceptual and logical data models are validated by client
 Data models are reconciled with appropriate level process models
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Function: Provide Data Assurance
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Activity: Integrate high-level business rules with code
 Data constraints required by the organization are implemented
 Database triggers, objects, and procedures are documented
 business rules are examined with impact on database determined
 Referential integrity and check constraints are enforced
Activity: Select unique identifiers and normalize the data model
 Define attributes of entities, primary natural keys and relationships
 Data model is in third normal form
 Logical and data models are validated by the client
 Logical model is validated against all transactions
Activity: Gather and document security requirements
 Security requirements are documented
 Potential security risks are identified and resolved
Function: Coordinate and Review
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Activity: Coordinate all modeling activities in workgroup
 Plan resource needs for upcoming projects
 Work with management to assign projects
 Follow up with sponsors on project progress
Activity: Review all models for consistency
 Review models for consistency across company
 Provide feedback to other data analyst on improvement opportunities
Skills
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Category: Business Concepts and Practices
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Category: Cognitive
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Conscientious
Flexible and adaptable
Category: Specific Databases
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Encryption
Firewalls (e.g., Network Address Translation, NAT)
Password practices and procedures
Category: Organizational
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Data warehousing
Distributed databases and processing
On-line transaction processing
Relational databases
Category: Network Security
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Bachelor's Degree in Technical discipline
Category: Database Processing
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Creation of forms and reports
Data flow and data structure modeling
Data management including modification, replication, clustering, and optimization
Data warehousing and data mining
Database concepts including tables, data types, instances, fields, and rows
Database security including access control, back-up and recovery
Query languages for database management system (e.g., SQL and PL/SQL)
Specific Database Management System Data Definition Language (DDL)
Specific Database Management System Data Manipulation Language (DML)
Category: Education
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Methods for trend analysis including forecasting system resources and space usage
Category: Databases
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Backup and recovery techniques
Code and scripts to move data between systems
Data conversion techniques
Legal requirements for archiving data
Techniques for data validation during and after conversion
Techniques to map data between old system and new one
Category: Database Performance Management
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Concurrency constraints within a system and across systems
Data cleaning, updating, editing and validation techniques
Data privacy rules, laws, and implementation techniques
Data storage and access within government regulation, and corporate policies
Developing database triggers, foreign key constraints, and validation (code) tables
User access rights to individual tables, rows, and columns (e.g., DDL SQL)
Category: Data Management, Backup and Recovery
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Analytical thinker
Detail oriented
Healthy skeptic
Independent thinker
Category: Data Management
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Business principles and business and industry-specific terminology
Business case analysis
Costing, budgeting, risk and financial analysis
Quality assurance concepts and procedures
Varies by site (e.g. DB2, Oracle, Access, Microsoft SQL Server)
Category: Specific Operating Systems
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Others may be required for specific sites (e.g. Unix, OS400)
Others would be useful (e.g. MVS, Linux, Mac OS, Sun OS)
Windows
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Category: Tools for Data Management
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RDBMS Catalog tools (e.g., Control Center, BMC Catalog Manager, Enterprise Manager)
Metadata tools (e.g. ERWin, Bachman, Oracle Designer)
Category: Work Experience
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10+ years experience in the field or a related area preferred
Works well with little to no supervision
Takes initiative
Self starter