BI and Data Analytics Engineer - EFX Data and BI

The candidate will be responsible for:

- Designing, developing, and maintaining BI dashboards, reports, and analytical solutions.

- Working with business users to understand reporting, KPI, and analytical requirements.

- Translating business needs into clear data requirements and technical solutions.

- Performing data analysis using SQL and Python to identify trends, issues, and opportunities.

- Supporting data quality checks, reconciliations, root-cause analysis, and exception reporting.

- Building automated data analysis, validation, and reporting processes using Python.

- Working with data from multiple sources, including databases, data lakes, APIs, and flat files.

- Supporting BI solutions connected to AWS-based data platforms.

- Collaborating with data engineers on data models, pipelines, integration logic, and data consumption layers.

- Helping improve the structure, performance, and usability of BI datasets.

- Creating clear documentation for dashboards, data definitions, metrics, lineage, workflows, and processes.

- Supporting the Data and BI Product Owner in prioritization, impact analysis, and delivery planning.

- Ensuring BI and analytics solutions are robust, controlled, secure, auditable, and suitable for a banking environment.

- Supporting the design and improvement of data workflows from source systems to BI and analytics consumption layers.

- Ensuring that data flows are reliable, controlled, well-documented, and aligned with business requirements.

- Monitoring and improving data quality across BI datasets, reports, dashboards, and analytical processes.

- Defining and implementing data quality checks, validation rules, reconciliations, and exception reporting.

- Investigating data quality issues, identifying root causes, and coordinating fixes with data engineering, source system, or business teams.

- Supporting proper data governance, including clear metric definitions, data ownership, lineage, documentation, and control points.

- Ensuring that BI reports and dashboards are based on trusted, validated, and well-understood data sources.

- Helping reduce manual data handling by improving automation, repeatability, and control of data processes.

- Supporting robust data workflows suitable for a banking environment, including auditability, access control, operational resilience, and traceability.

- Hands-on experience with BI tools such as Power BI, Tableau, Qlik, or similar.

- Ability to design clear, user-friendly dashboards and reports.

- Good understanding of KPIs, metrics, dimensions, measures, drilldowns, and data visualization best practices.

- Experience working with business stakeholders to gather, challenge, and refine reporting requirements.

- Understanding of semantic layers, reusable datasets, and governed reporting models.

-Strong SQL skills.

- Ability to analyze large and complex datasets.

- Experience with data profiling, data validation, reconciliation, and data quality checks.

- Strong Python skills for data analysis, automation, and data validation.

- Experience with libraries such as pandas, NumPy, boto3, PySpark, or similar.

- Ability to automate manual reporting, data extraction, data validation, and reconciliation processes.

- Ability to create scripts for data extraction, transformation, analysis, quality checks, and exception reporting.

- Understanding of how Python can support scalable and repeatable data workflows.

- Understanding of relational databases, data warehouses, data marts, and analytical data models.

- Ability to investigate data issues independently and explain findings clearly to business and technical stakeholders.

The candidate should have practical experience with AWS data services, ideally including some of the following:

- Amazon ElastiCache

- Amazon Timestream

- Amazon Athena

- AWS Glue

- Amazon MWAA

- Amazon SageMaker Studio

- Amazon QuickSight

- Cloud-based data lake and data warehouse concepts.

- How to work with cloud-hosted datasets.

- How to support BI integration with AWS data platforms.

- Basic cloud security, access control, monitoring, and operational controls.

Experience in banking, financial services, financial markets, trading, risk, treasury, or regulatory reporting.

- Knowledge of FX or EFX data would be a strong advantage.

- Understanding of controlled environments, data governance, access management, auditability, and operational resilience.

- Experience working with sensitive, regulated, or business-critical data.

- More technical than a traditional BI analyst.

- Comfortable working between business and technology teams.

- Strong in problem-solving and analytical thinking.

- Able to investigate data issues independently.

- Able to challenge requirements and propose better analytical solutions.

- Detail-oriented, especially around data quality, metric definitions, and controls.

- Able to work in an agile, product-oriented environment.

- Proactive and ownership-driven.

- Comfortable with ambiguity and complex banking data landscapes.

- Able to communicate technical topics clearly to non-technical stakeholders.

- Focused on building trusted, controlled, and reusable data and BI solutions.

Nice to have:

-Knowledge of FX, EFX, trading platforms, pricing, orders, executions, market data, or client flow.

- Experience with Databricks, Snowflake, dbt, Airflow, or Spark.

- Experience with CI/CD, Git, Jira, Confluence, or DevOps practices.

- Basic understanding of data governance, lineage, metadata, and data cataloguing.

- Experience building semantic layers or reusable BI datasets.

- Experience with performance optimization for BI dashboards and SQL queries.

- Experience designing monitoring or alerting for data quality and reporting processes.