Data Analyst - ERP Project

About the Job:

• Understanding project requirements by conducting Business Discovery Sessions.
• Collaborating with internal teams such as system support teams, DBA, and MIS teams to translate existing requirements into desired marts and reports.
• Preparing data mapping documents necessary for the development of various layers of the data platform (raw – bronze, EDM – silver, project specific – gold).
• Supporting various phases of projects as needed by 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.

Key Qualifications:

• Strong analytical and problem-solving skills for effective design of data solutions.
• Knowledge and understanding of Banking Retail, Business & Wholesale products.
• Attention to detail and a commitment to delivering high-quality work.
• Excellent communication and teamwork skills for collaboration with cross-functional teams.
• Ability to manage time effectively and prioritize tasks in a dynamic work environment.

Technical Skills:

• Proficient in data querying and processing using SQL, Oracle, Apache Spark, or similar languages.
• Experience with data visualization tools such as Power BI, Business Objects, Crystal, or similar.
• Understanding of data warehousing and ETL concepts.

Competencies:

• Expert-level proficiency in SQL and Python; knowledge of SAS/R/Spark is a 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 relevant to performance-critical data engineering.