Client Reporting Modernization- Full Stack Engineer (AL/ML)- WLK
About The Position
The Full Stack Engineer will work collaboratively in a multi-developer Agile environment to deliver high-quality data engineering and analytics solutions, following coding standards and DevOps practices. The role focuses on data engineering, data modeling, large-scale data processing, and building data pipelines and ETL/ELT workflows, including solutions that support AI/ML model development and productionization. The position involves working with cloud-based data platforms, particularly Snowflake data warehousing and data lake architectures, and integrating data from structured and unstructured sources. The engineer will build data-centric APIs and microservices for AI/ML integration and downstream analytics, develop interactive Power BI dashboards and business intelligence solutions, and build user interfaces for data products and AI-driven insights using modern front-end technologies. The role also supports data quality, testing, validation, reliability, observability, and performance optimization. It includes designing data architectures and model data pipelines for AI/ML workloads and using streaming and messaging technologies for real-time data processing. Strong analytical and problem-solving skills, customer understanding, and a willingness to learn and experiment with new technologies are important.
Requirements
- At least 5 years of proven experience designing, building, and deploying data pipelines, ETL/ELT workflows, and AI/ML data solutions, including support for model development and productionization.
- Outstanding Python and SQL skills.
- Deep experience in data engineering, data modeling, and large-scale data processing across modern data platforms.
- Extensive experience with cloud-based data platforms, including AWS and Microsoft Azure, with a strong focus on Snowflake-based data warehousing and data lake architectures.
- Strong expertise in building and optimizing Snowflake data pipelines and data models.
- Experience integrating data from multiple structured and unstructured sources.
- Experience designing and consuming data-centric APIs and microservices.
- Extensive experience building Power BI dashboards, data visualizations, and business intelligence solutions, including semantic modeling and performance optimization.
- Strong front-end development experience with JavaScript, TypeScript, and modern frameworks such as Angular or React.
- Experience with data quality, testing, and validation frameworks for data pipelines, and familiarity with automated testing in data engineering environments.
- Experience applying data reliability, observability, and performance optimization practices.
- Experience designing data architectures for AI/ML workloads and model data pipelines.
- Hands-on experience with streaming and messaging technologies such as Kafka, RabbitMQ, or Service Bus.
- Consistent record of accomplishment working collaboratively to deliver high-quality data engineering and analytics solutions in a multi-developer Agile environment.
- Strong analytical, technical, and problem-solving skills to understand complex customer needs and transactions.
- Ability to learn and experiment with new technologies and patterns.
Responsibilities
- Design, build, and deploy data pipelines, ETL/ELT workflows, and AI/ML data solutions.
- Support AI/ML model development and productionization.
- Build and optimize data pipelines and data models in Snowflake, integrating structured and unstructured data sources.
- Design and consume data-centric APIs and microservices for AI/ML model integration and downstream analytics.
- Build interactive dashboards, data visualizations, and business intelligence solutions using Power BI, including semantic modeling and performance optimization.
- Develop user interfaces for data products and AI-driven insights using JavaScript, TypeScript, and modern frameworks such as Angular or React.
- Apply data quality, testing, and validation approaches to data pipelines.
- Use data reliability, observability, and performance optimization practices to support scalable and resilient data platforms.
- Design data architectures for AI/ML workloads and model data pipelines.
- Use streaming and messaging technologies to enable real-time data processing pipelines.
- Collaborate in a multi-developer Agile environment and follow coding standards and DevOps practices.
- Understand complex customer needs and transactions, and learn and experiment with new technologies and patterns.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
