Senior Analyst In Data & Analytics at Datacentrix

Managed Talents Solutions' client is looking for a Senior Analyst in Data & Analytics with 3+ years of developing and managing data products from ingestion to operations as well as experience supporting data analytics solutions.  Must have 3+ years experience using Python and working with relational SQL databases, experience using CI/CD tools and using Azure platform for Data and Analytics products specially focused in Data Services : CosmosDB, DynamoDB, Databricks, Glue, DataLake, Redshift, etc.

Must have requirements:

• Bachelor’s Degree in a related field or equivalent required
• 3+ years of developing and managing data products from ingestion to operations.
• 3+ years of experience supporting data analytics solutions.
• 3+ years of experience in data storytelling and data visualization using tools like PowerBI, Tableau or Google Analytics.
• Experience using Python and working with relational SQL databases
• Experience using CI/CD tools
• Solid knowledge about data modeling, architecture, ETL and cloud technologies such as AWS, Azure, Alteryx, etc.
• Demonstrated proficiency in using Azure platform for Data and Analytics products specially focused in Data Services (CosmosDB, DynamoDB, Databricks, Glue, DataLake, Redshift etc.)
• Experience in Agile\DevOps project methodology.

You will be required to:

• Analyze business analytical requirements (e.g., functional and technical requirements, data need, business processes) by conducting information-gathering sessions with stakeholders, analyzing the information and prioritizing the requirements to gain understanding of the business needs.
• Develops data analytics products (new data ingestion, data processing ETL, data enhancements, master data, data integration). Connect business processes to data product functionality and ensure the reuse, integration, maintainability and efficiency.
• Develop logical data model based on business requirements in order to determine the completeness and quality of the information and process components (e.g., Data Flow Diagram, Entity Relationship Models).
• Engages with Data governance team to define required data quality controls and DevOps process for data products and implement them for data and analytics solutions.