Data Analyst - ERP Project

Understanding the project requirements, by conducting Business Discovery Sessions

 Work with internal teams like system support teams, DBA and MIS teams to translate the existing requirements into

the desired mart and reports

 Prepare data mapping documents, needed for development of various layers of the data platform (raw – bronze,

EDM – silver, project specific – gold)

 Support during various phases of the projects as needed by the data engineers, data modelers, QA testers and Power

BI developers

 Collaborating with other developers, data analysts, and stakeholders to ensure that the software meets the needs of

the business or organization

Strong analytical and problem-solving skills to effectively design data solutions.

• Banking Retail, Business & Wholesale Product Knowledge and understanding
• Attention to detail and a commitment to delivering high-quality work.
• Excellent communication and teamwork skills to collaborate with cross-functional teams.
• Ability to manage time effectively and prioritize tasks in a dynamic work environment.
  
  

 Technical Skills

• Strong data querying and processing skills using SQL, Oracle, Apache Spark or similar language
• Data Visualization tools – Power BI, Business Objects, Crystal or similar tool
• Data Warehousing and ETL concepts
  
  

 Competencies

• Expert-level proficiency in SQL, Python with SAS/R/Spark as plus
• Deep understanding of distributed data processing frameworks, particularly Apache Spark.
• Experience with cloud platforms (Azure, AWS) — including Object storage, compute, networking, and data
  
  

integration services.

• Familiarity with data modeling techniques, including schema design, partitioning, and performance tuning.
• Experience working with structured and semi-structured data formats (e.g., JSON, Parquet, Avro, XML).
• Knowledge of data governance, quality assurance, and compliance best practices.
• Solid grasp of core computer science fundamentals (e.g., data structures, search algorithms, queues)
  
  

relevant to performance-critical data engineering