Senior Data Engineer - Ag & Trading

• The Senior Professional, Data Engineering job designs, builds and maintains complex data systems that enable data analysis and reporting. With minimal supervision, this job ensures that large sets of data are efficiently processed and made accessible for decision making.

• Data infrastructure: Prepares data infrastructure to support the efficient storage and retrieval of data.
• Data formats: Examines and resolves appropriate data formats to improve data usability and accessibility across the organization.
• Data analytical solutions: Develops complex data products and solutions using advanced engineering and cloud based technologies, ensuring they are designed and built to be scalable, sustainable and robust.
• Data pipelines: Develops and maintains streaming and batch data pipelines that facilitate the seamless ingestion of data from various data sources, transform the data into information and move to data stores like data lake, data warehouse and others.
• Data systems: Reviews existing data systems and architectures to identify areas for improvement and optimization.
• Stakeholder management: Collaborates with multi-functional data and advanced analytic teams as well as with business teams to gain requirements and ensure that data solutions meet the functional and non-functional needs of various partners.
• Data frameworks: Builds complex prototypes to test new concepts and implements data engineering frameworks and architectures that improve data processing capabilities and support advanced analytics initiatives.
• Automated deployment pipelines: Develops automated deployment pipelines improving efficiency of code deployments with fit for purpose governance.
• Data modeling: Performs complex data modeling in accordance to the datastore technology to ensure sustainable performance and accessibility.

Minimum requirement of 8 years of relevant work experience in :

• Data Platform Design: Designs and builds scalable ELT data platforms on AWS for batch and real-time workloads. Selects appropriate services and storage patterns, and structures solutions for performance, maintainability, security and cost efficiency.
• Advanced Python Engineering: Develops production-grade Python pipelines, services and reusable frameworks. Apply modular design, error handling, logging, configuration management, testing and code review practices.
• Snowflake and Relational Database Expertise: Demonstrates deep knowledge of Snowflake architecture and advanced SQL, including data modelling, query optimization and workload management. Has experience with relational databases such as Oracle, SQL Server and PostgreSQL, including schema design, transactions and performance tuning.
• Batch and Real-Time Processing: Designs, develops and operates reliable batch and streaming pipelines using Apache Kafka. Handles event processing, schema changes, retries, ordering, duplicate events and failure recovery, while supporting accurate and timely data delivery.
• Performance and Cost Optimization: Monitors and improves Snowflake queries, warehouse usage and Python workloads. Identifies performance bottlenecks, manages compute resources, and balances cost, throughput and latency as data volumes grow.
• Security and Governance: Implements role-based access controls, data protection and secure data-sharing patterns across platforms. Applies least-privilege access, manages sensitive data appropriately and supports traceability and governance requirements.
• Reliability and Data Quality: Builds resilience into data pipelines through validation, error handling, retries, monitoring and alerting. Establishes data quality checks and investigates failures to help ensure critical datasets are complete, accurate and available.
• GenAI Enablement: Prepares structured and unstructured data for GenAI use cases, including data cleaning, chunking, metadata enrichment and embedding generation. Supports integration with OpenAI and retrieval-augmented generation (RAG) workflows, while considering data quality, access controls, security and traceability.
• React and .NET Knowledge: Familiarity with React and .NET is an advantage, particularly for contributing to user interfaces, APIs and service integrations that consume or support data products.
• Preferred Competencies: Proven experience working in the Trading and / or Finance industry.