As a data analyst your main role is understanding business requirements and provide 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.
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
Technical Skills
- Strong data querying and processing skills using SQL, SAS, 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
