Role: Technical Business Analyst Location: Phoenix, AZ Duration: 18 months Experience: 8 to 9 years Position: 2 Job Skills:

Advanced SQL Proficiency: They should have a strong command of SQL to query, manipulate, and join datasets efficiently. This skill is crucial for unifying the current contact data with 3rd party vendor data.

Data Validation: Ability to clean and standardize data, identify duplicates, and resolve inconsistencies is crucial for maintaining data quality.

Problem-Solving Skills: Strong problem-solving skills are necessary for optimizing processes and resolving technical challenges encountered during the project.

Data Skills: Investigate data problems and perform deep-dive analyses. Ability to identify trends and interpret data. Should be able to summarize their findings in simple yet consumable structure (via slides/tables).

Additional skills required for the technical data analyst:

Database Management: Understanding of database management principles, including indexing, performance optimization, and data security, is essential for managing the unified dataset efficiently.

Data Integration and ETL (Extract, Transform, Load): Experience with ETL processes is necessary for merging and transforming datasets from various sources. The consultant should be capable of designing and implementing data integration pipelines to enrich existing contact data. (most relevant for TAU expansion)

Python Programming: Proficiency in Python is essential for developing scripts and automation tools to streamline the data enrichment process. Knowledge of Regular Expression (Regex) to identify and manipulate text strings such as Job titles, names, and email domains is advantageous.

Job Responsibilities:

Data Integration and Enrichment: Collaborate with the project team to integrate vendor data with existing contact data using SQL and Python scripts, ensuring accuracy and consistency.

Automation Mindset: Partner closely with the Data Engineering team to design an automated solution to refresh data and validate contacts. Integrate error handling, data quality checks, and monitoring mechanisms to ensure accuracy and reliability.

Performance Monitoring and Optimization: Monitor the performance of data integration and enrichment processes, identify bottlenecks and drops in coverage.

Analysis: Investigate data problems and perform deep-dive analyses to help guide recommendations. This includes having the ability to analyze third-party data against internal data to determine the potential success of data enhancement projects. You must be able to identify areas where data is helpful vs not and be able to summarize trends and the root cause of any issues.

Documentation and Reporting: Document processes, methodologies, and solutions. Provide knowledge transfer to enable internal teams to consume data for their analysis & data science models.