Egis

Data Engineer

• The Senior Data Engineer is responsible for designing, implementing, maintaining, and optimizing a cloud-based data architecture and data pipeline ecosystem.
• The position supports advanced analytics, machine learning operations, fraud detection initiatives, and investigative activities by delivering scalable, secure, and sustainable Azure-based data solutions.
• The Senior Data Engineer develops and maintains modern ELT/ETL pipelines, data models, source-controlled environments, and operational standards that enable efficient data ingestion, processing, storage, and access.
• Design, implement, and maintain scalable Azure-based data architecture supporting audits, investigations, and fraud analytics.
• Develop, optimize, and sustain ELT/ETL pipelines within Azure Synapse Analytics and Azure Machine Learning environments.
• Migrate and integrate large-scale datasets into Azure Data Lake Storage (ADLS).
• Establish source control, version management, and development standards across data engineering assets.
• Implement pipeline monitoring, validation, logging, and error-handling frameworks.
• Design and maintain data models, data dictionaries, entity relationship diagrams, and architectural documentation.
• Optimize ingestion, transformation, storage, and retrieval performance across diverse data sources and formats.
• Develop self-service data access capabilities for analysts and investigators.
• Collaborate with Data Scientists to ensure infrastructure effectively supports machine learning and AI initiatives.
• Author and maintain Standard Operating Procedures (SOPs) governing data pipeline development, deployment, and monitoring.
• Evaluate emerging AI-enabled engineering tools and LLM-assisted automation capabilities.
• Recommend and implement architectural improvements that increase efficiency, reliability, security, and cost effectiveness.

Senior Data Engineer

Data Scientist

• Provide advanced analytics, machine learning, and investigative data support.
• Work closely with criminal investigators, auditors, and technical stakeholders.
• Develop and maintain predictive models and fraud detection methodologies.
• Implement NLP solutions and analytical products for identifying financial fraud.
• Transform complex datasets into actionable intelligence using statistical methods.
• Analyze large datasets to identify fraud indicators.
• Document methodologies, models, and analytical results in accordance with requirements.
• Collaborate with Data Engineering to ensure system architecture supports analytics.

Senior Data Engineer

Senior Data Scientist