Client Reporting Modernization- Full Stack Engineer (AL/ML)- WLK

Consistent record of accomplishment of working in collaborative teams to deliver high quality data engineering and analytics solutions in a multi-developer agile environment following coding standard methodologies and DevOps practices. Outstanding Python and SQL skills, with deep experience in data engineering, data modeling, and large-scale data processing across modern data platforms. At least 5 years of proven experience designing, building, and deploying data pipelines, ETL/ELT workflows, and AI/ML data solutions, including experience supporting model development and productionization. Extensive experience working with cloud-based data platforms (AWS, Microsoft Azure) with a strong focus on Snowflake-based data warehousing and data lake architectures. Strong expertise in building and optimizing data pipelines and data models in Snowflake, along with experience integrating data from multiple structured and unstructured sources. Experience designing and consuming data-centric APIs and, Title: Full Stack Engineer (Go) Location: Westlake, TX On-site/Remote/Hybrid: Onsite Duration: 6+ Months Interview Process: 2 Rounds No of submissions: No of Positions: …

• 2 months ago, Consistent record of accomplishment of working in collaborative teams to deliver high quality data engineering and analytics solutions in a multi-developer agile environment follow…
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microservices to support AI/ML model integration and downstream analytics use cases. Extensive experience building interactive dashboards, data visualizations, and business intelligence solutions using Power BI, including semantic modeling and performance optimization. Strong experience with front-end web development technologies, including JavaScript, TypeScript, and modern frameworks such as Angular or React, with the ability to build intuitive user interfaces for data products and AI-driven insights. Experience with data quality, testing, and validation frameworks for data pipelines, along with familiarity with automated testing approaches in data engineering environments. Experience applying data reliability, observability, and performance optimization practices to ensure highly scalable and resilient data platforms. Experience designing and implementing data architectures supporting AI/ML workloads and model data pipelines. Hands-on experience working with data streaming and messaging technologies such as Kafka, RabbitMQ, or Service Bus to enable real-time data processing pipelines. Strong analytical, technical, and problem-solving skills to understand complex customer needs and transactions Ability to learn and experiment with new technologies and patterns