Pricing Senior Data Analyst

Responsibilities: 

Analyze complex data to identify patterns, detect anomalies in data using statistical tools as well as machine learning algorithms if required.  

Find workable solution in case of data inconsistency and inconclusive data 

Drive projects with minimal guidance. Provide thought leadership by researching best practices and conducting experiments. 

Work with cross functional group consisting of Engineering, Product, Program, Marketing, Sales managers to drive data-based decisions. 

Lead major analytical and Operational excellence projects across multi-functional team and enable stakeholders to make data driven decisions to maximize customer experience/Business impact at minimal possible cost.  

 Hands on experience with BI Tools, ETL, data processing, database programming and data analytics 

 Proficient is Sql and no-sql languages, R, Python, Advanced Excel 

 Worked on gathering data from Kafka, Splunk. Work with big data on GCP/ Azure/ Snowflake/Cloudera 

Accessing data in data warehouse pragmatically using connectors. 

 Handled multi-million records of data. Troubleshooting and fixing data issue 

 Data Visualization in any BI tools like Tableau, PowerBI, etc. DAX Programming. 

 Understanding and application of statistical concepts to solve business problems. 

Advanced Statistical modelling Knowledge discovery in databases (KDD Skills). 

Performing Translation on Global reports if needed. 

Building Automated Pipelines using Automation tools. 

Requires knowledge of Analytics/big data analytics / automation techniques and methods; Business understanding; Precedence and use cases; Business requirements and insights.  

To translate/ co-own business problems within one’s discipline to data related or mathematical solutions.  

Identify appropriate methods/tools to be leveraged to provide a solution for the problem. Share use cases and gives examples to demonstrate how the method would solve the business problem.  

Understanding Business Context: Requires knowledge of Industry and environmental factors; Common business vernacular; Business practices across two or more domains such as product, finance, marketing, sales, technology, business systems, and human resources and in-depth knowledge of related practices; Directly relevant business metrics and business areas.  

To Provide recommendations to business stakeholders to solve complex business issues.  

Develop business cases for projects with a projected return on investment or cost savings. Translate business requirements into projects, activities, and tasks and aligns to overall business strategy and develops domain specific artifact.  

Serve as an interpreter and conduit to connect business needs with tangible solutions and results. Identify and recommend relevant business insights pertaining to their area of work.  

Data Source Identification: Requires knowledge of Functional business domain and scenarios; Categories of data and where it is held; Business data requirements; Database technologies and distributed datastores (e.g. SQL, NoSQL); Data Quality; Existing business systems and processes, including the key drivers and measures of success.  

To support the understanding of the priority order of requirements and service level agreements. Help identify the most suitable source for data that is fit for purpose. Perform initial data quality checks on extracted data.  

Data Visualization: Requires knowledge of Visualization guidelines and best practices for complex data types; Multiple data visualization tools (for example, Python, R libraries, GGplot, Matplotlib, Ploty, Tableau, PowerBI etc.); Advanced visualization techniques/ tools; Communication & influencing technique; Emotional intelligence.  

To generate appropriate graphical representations of data and model outcomes.  

Understand customer requirements to design appropriate data representation for multiple data sets.  

Present to and influences the team and business audience using the appropriate data visualization frameworks and conveys clear messages through business and stakeholder understanding. Customize communication style based on stakeholder under guidance and leverages rational arguments. Guide and mentor junior associates on story type. 

Data Quality Management: Requires knowledge of Data quality management techniques and standards; Business metadata definitions and content data definitions; Data profiling tools, data cleansing tools, data integration tools, and issues and event management tools; Understanding of user’s data consumption, data needs, and business implications; Data modeling, storage, integration, and warehousing; Data quality framework and metrics; 

Assist in the planning, design and implementation of an exploratory data analysis research projects.  

Qualification: 

Bachelor’s degree in engineering, Math, Computer Science, Operations Research, Statistics and other related degrees. 

Business or Analytics Related Certifications.  

Experience 
 

3-5 years of experience or relevant work experience in analytics, finance, or consulting roles. Big-Data 

Also, Business Domain Certifications will be added advantage. 

Skills 

Power BI, Advanced Excel, Macros, Tableau, SQL, Python