Lead Data Analyst - Banking

Job Description Lead Data Analyst - Banking - ( CREQ
244580 ) Description

As a data analyst, your main role is understanding business requirements and providing data mapping and analysis that forms the blueprint of data flow from raw source systems to a customized data mart, dashboards, or any other API required for data consumption. You will play a crucial role in the data management process, ensuring that data is collected, transformed, and made accessible for analytical purposes.

Your expertise in data technologies will enable the organization to make informed decisions and derive valuable insights from large datasets.

• Understand the project requirements by conducting Business Discovery Sessions.
• Work with internal teams such as system support teams, DBA, and MIS teams to translate existing requirements into the desired data mart and reports.
• Prepare data mapping documents needed for the development of various layers of the data platform (raw – bronze, EDM – silver, project-specific – gold).
• Support various phases of the projects as needed by data engineers, data modelers, QA testers, and Power BI developers.
• Collaborate with other developers, data analysts, and stakeholders to ensure that software meets business or organizational needs.

• Strong data querying and processing skills using SQL, SAS, Oracle, Apache Spark or similar languages.
• Data visualization tools – Power BI, Business Objects, Crystal, or similar tools.
• Data warehousing and ETL concepts.

• 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.