Overview
Rhino Partners is seeking a Data Engineer to support the design, development, and optimisation of scalable data platforms and pipelines. The role focuses on building reliable data ingestion and transformation workflows that enable data-driven products, analytics, and AI initiatives.
The successful candidate will work closely with cross-functional teams including Data Architects, Business Analysts, Frontend Engineers, and Product stakeholders to integrate disparate data sources and deliver production-grade data solutions on modern cloud platforms.
Key Responsibilities
Data Pipeline Engineering
- Design, develop, and maintain scalable data pipelines that extract data from multiple sources, transform it according to business requirements, and load it into downstream systems.
- Implement ETL/ELT workflows to support operational systems, analytics platforms, and data products.
- Build and maintain large-scale batch and real-time data processing pipelines using modern data processing frameworks.
Data Integration & Platform Development
- Integrate and consolidate data from multiple systems and data silos into scalable, governed data platforms.
- Design and optimise data flows for performance, reliability, and maintainability.
- Support development of data lakes, data warehouses, and data marts to enable efficient storage and retrieval.
Collaboration & Delivery
- Work closely with Project Managers, Data Architects, Business Analysts, and Developers to deliver scalable data-driven applications.
- Participate in Agile development processes, including sprint planning, code reviews, and continuous delivery.
- Contribute to pair programming, code quality practices, and engineering standards across the team.
Data Governance & Security
- Ensure pipelines comply with data governance policies, security standards, and access control practices.
- Implement secure handling of data across ingestion, transformation, and storage layers.
Skills & Experience
Core Technical Skills
- Bachelor's degree in Computer Science, Software Engineering, or a related field.
- 35 years of experience in data engineering, ETL, or data integration projects.
- Strong proficiency in SQL and Python for data extraction, transformation, and processing.
- Experience designing and building production-grade ETL pipelines.
Data Engineering Tools & Platforms
Hands-on experience with:
Data Integration / ETL
- SQL Server Integration Services (SSIS)
- Python-based data processing pipelines
- Snowflake data platform
Cloud Platforms
- Experience working with Government Commercial Cloud (GCC / GCC+) environments such as AWS or Azure.
AWS Data Ecosystem (Preferred)
- AWS Lambda
- ECS Container Tasks
- EventBridge
- AWS Glue
Databases & Storage
Experience working with:
- AWS S3, RDS, SQL-based databases
- Additional experience with PostgreSQL, Athena, MongoDB, MySQL, Cassandra, SQLite, or VoltDB is advantageous.
DevOps & Infrastructure
- Experience working with CI/CD pipelines (e.g. GitLab).
- Familiarity with Infrastructure as Code / automation tools such as:
- Terraform
- Ansible
- Puppet
- Vagrant
Data Architecture & Modelling
- Familiarity with data platform concepts such as:
- Data Lakes
- Data Warehouses
- Data Marts
- Data Virtualisation
Additional Knowledge
- Familiarity with REST APIs and web protocols.
- Understanding of data governance, access control, and security best practices.
- Knowledge of system design, data structures, and algorithms.
- Exposure to AI/ML data pipelines, including concepts such as RAG (Retrieval-Augmented Generation) and Model Context Protocol (MCP), is advantageous.
Working Environment
- Comfortable working in both Linux and Windows development environments.
- Interest in bridging data engineering and analytics teams to deliver impactful data products.
