Source description
About the role
Data Lead (Databricks + Power Bi)
Responsibilities
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API Integration & Data Extraction
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Lead the integration of Fenergo APIs to extract relevant KYC and AML data, ensuring seamless connectivity and data flow between systems
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Design, develop, and maintain scalable data pipelines and ETL processes to support data ingestion from various sources, including databases, APIs, and flat files
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Ensure robust data extraction processes that maintain data quality and compliance with regulatory requirements
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Data Processing & Pipeline Development
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Utilize Databricks and Apache Spark to design and implement robust data processing pipelines, ensuring high data quality and performance
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Work with DataFrames for transforming data and implementing the Medallion Architecture
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Execute SQL queries for data extraction, manipulation, and complex data operations
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Join datasets and add fields to reports to provide comprehensive analytical insights
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Leverage AI tools in Databricks to assist with data workflows and optimization
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Use notebooks as data transformation pipelines for efficient data processing
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Data Analysis & Interpretation
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Analyze and interpret complex data sets to identify trends, patterns, and anomalies that can inform business decisions related to client and investor lifecycle management
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Understand and navigate the data model to ensure accurate data representation and reporting
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Conduct regular data quality assessments and audits to ensure data integrity and compliance with industry standards
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Perform root cause analysis to swiftly identify data issues and collaborate with relevant teams to implement effective solutions
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Data Visualisation & Reporting
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Architect and develop interactive dashboards and reports in Power BI, translating complex data into clear, actionable insights for clients, leadership, and stakeholders
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Develop and maintain dashboards and reports to provide insights into key performance indicators (KPIs) and operational metrics for KYC/AML processes
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Create visual representations that highlight critical data points for regular reporting to clients and senior management
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Ensure reports meet the needs of both technical and non-technical stakeholders
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Collaboration & Stakeholder Management
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Collaborate with cross-functional teams, including IT, Compliance, Risk Management, business analysts, and senior management, to gather data requirements and deliver strategic insights
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Engage with clients and internal stakeholders to understand their reporting needs and ensure alignment with business objectives
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Work closely with KYC/AML operations teams to ensure data solutions support compliance and regulatory requirements
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Act as a bridge between technical teams and business users, translating complex data concepts into actionable business insights
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Documentation & Governance
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Maintain comprehensive documentation of data processes, API integrations, data flows, data management processes, and reporting solutions for future reference and compliance
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Document data governance practices and ensure adherence to data quality best practices
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Ensure all data handling complies with regulatory standards and internal policies
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Continuous Improvement & Problem-Solving
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Recommend long-term product solutions to enhance data quality, accessibility, and usability
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Identify opportunities for process optimization and automation in data workflows
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Stay up-to-date with industry trends and best practices in data engineering, analysis, and management
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Proactively identify and resolve data-related issues, ensuring timely and accurate reporting
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Demonstrate creativity and insightfulness in developing dynamic approaches to complex data challenges
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Quality Assurance
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Ensure data integrity throughout all pipelines and reporting mechanisms
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Implement data validation and quality control measures
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Monitor data processes and implement control mechanisms to ensure reliability
Skills
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Core Data Engineering Skills (Required):
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Proficiency in Databricks and Apache Spark for data processing and pipeline development
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Strong knowledge of Power BI for data visualization and reporting, with ability to create executive-level dashboards
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Expert-level proficiency in SQL for data querying, manipulation, and complex analytical operations
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Experience with programming languages such as Python or R for data analysis and automation
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Strong understanding of data warehousing concepts and ETL processes
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Knowledge of data modeling concepts and best practices for data management
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Understanding of the Medallion Architecture and data lakehouse principles
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Experience working with DataFrames for data transformation
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Ability to leverage AI tools in Databricks to optimize data workflows
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API & Integration Skills (Required):
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Strong experience in API integration for data extraction and system connectivity
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Ability to ensure seamless data flow between multiple systems
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Cloud & Infrastructure (Required):
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Experience with cloud platforms, particularly Azure or AWS
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Knowledge of Git connection to Databricks for version control
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Experience with AWS/Azure and Databricks integration/mounting
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Understanding of data governance and data quality best practices
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Additional Technical Skills (Preferred):
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Databricks administration skills
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Familiarity with machine learning concepts and their application in data analysis
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Experience with graph data models
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Understanding of data governance and compliance standards (GDPR, AML regulations, etc.)
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Knowledge of secure data handling practices
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