Data Analyst – AWS Data Platform
Key Skills for This Role
Full Job Posting
Experience
- A minimum of 5 years of experience is required.
- Strong experience in data analysis, business intelligence, or data platform delivery is required, including work with large and complex enterprise datasets.
- Experience supporting data migration, transformation, or data platform initiatives is required.
Technical and Functional Skills
- Advanced SQL skills are required, including querying, joins, aggregations, reconciliation, and investigation of large datasets.
- Experience with data profiling, validation, and reconciliation is required.
- Strong understanding of data quality, data mapping, data lineage, business rules, data transformation, and reference and master data is required.
- Experience translating business requirements into detailed analytical and data requirements is required.
- Experience defining and validating KPIs, measures, and business calculations is required.
- Understanding of relational databases, data warehouses, and modern data lake or lakehouse environments is required.
- Experience with analytical and visualization tools such as Power BI is required.
- Working knowledge of Python for data analysis is advantageous.
- Preferred data platform technologies include Amazon S3, Amazon Redshift, Amazon Athena, and AWS Glue.
Domain and Collaboration
- Banking or financial services experience is strongly preferred.
- Relevant banking data domains include customer, account, product, transaction, finance, risk, and treasury.
- The role involves working with business stakeholders, data engineers, data modelers, and data architects.
- Understanding data governance, security, and regulatory requirements in a controlled enterprise environment is required.
- Strong stakeholder-management, communication, analytical, and problem-solving capabilities are required.
Responsibilities
- Analyze large and complex datasets across multiple source systems and identify inconsistencies or anomalies.
- Profile, validate, and reconcile data, including investigation of large datasets.
- Translate business requirements into detailed analytical and data requirements.
- Define and validate KPIs, measures, and business calculations.
- Support data migration, transformation, and data platform initiatives.
- Work alongside business stakeholders, data engineers, data modelers, and data architects.
- Communicate complex data findings in clear business language and actionable insights.
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