As a Lead Data Analyst, your main role involves understanding business requirements and providing data mapping and analysis that forms the blueprint for data flow from raw source systems to customized datamarts, dashboards, or any other APIs needed for data consumption. You will play a crucial role in the data management process, ensuring data is collected, transformed, and made accessible for analytical purposes. Your expertise in data technologies will be vital for the organization to make informed decisions and derive valuable insights from large datasets.
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Key Responsibilities:
- Understand project requirements by conducting Business Discovery Sessions.
- Collaborate with internal teams, including system support, DBA, and MIS teams, to translate existing requirements into desired marts and reports.
- Prepare data mapping documents for the development of various layers of the data platform (raw – bronze, EDM – silver, project-specific – gold).
- Support various project phases as needed by data engineers, data modelers, QA testers, and Power BI developers.
- Collaborate with other developers, data analysts, and stakeholders to ensure software meets business needs.
Technical Skills:
- Strong data querying and processing skills using SQL, SAS, Oracle, Apache Spark, or similar languages.
- Proficient in data visualization tools such as Power BI, Business Objects, Crystal Reports, or similar tools.
- Knowledge of data warehousing and ETL concepts.
Competencies:
- Expert-level proficiency in SQL and Python; experience with SAS/R/Spark is a plus.
- Deep understanding of distributed data processing frameworks, particularly Apache Spark.
- Familiarity with cloud platforms (Azure, AWS), including object storage, compute, networking, and data integration services.
- Experience with structured and semi-structured data formats (e.g., JSON, Parquet, Avro, XML).
- Knowledge of data governance, quality assurance, and compliance best practices.
- Solid understanding of core computer science fundamentals (e.g., data structures, search algorithms, queues) relevant to performance-critical data engineering.
