Data Analyst - ERP Project

Job Description

·       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