Job Description
We are looking for a hands-on Senior Data Engineer who can design, build, and operate robust cloud-native data and AI solutions. The ideal candidate combines strong software engineering fundamentals with deep practical experience in AWS and Snowflake.
You Will Have The Following Responsibilities
Design, build, and operate production-grade data solutions end-to-end, from architecture and implementation through deployment, monitoring, and continuous improvement.
Design and implement reliable, scalable, secure, and well-governed data pipelines and data products using AWS and Snowflake across structured, semi-structured, and unstructured data sources.
Model, curate, and optimize Snowflake datasets, schemas, and data structures to ensure performance, data quality, consistency, governance, and usability.
Apply software engineering best practices including clean code, modular design, automated testing, CI/CD, observability, secure development, and maintainable architecture.
Partner with business and technical stakeholders to translate business requirements into scalable data engineering solutions and identify opportunities for analytics and AI initiatives.
Build cloud-native integrations and automation using AWS services including compute, storage, networking, security, orchestration, and serverless technologies.
Utilize AI-assisted engineering tools to improve coding, testing, documentation, debugging, and overall engineering productivity while maintaining high-quality standards.
Own deployment, release management, production support, troubleshooting, root cause analysis, performance tuning, and incident resolution.
Participate in peer code reviews, pair programming, and continuous improvement of engineering standards and practices.
You Will Have The Following Qualifications
Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, Information Technology, Data Science, Artificial Intelligence, or a related technical discipline.
At least 7 years of hands-on experience in Data Engineering, Software Engineering, or Cloud Engineering, with a proven track record of delivering enterprise-grade data solutions.
Demonstrated experience building and supporting cloud-native data platforms, data pipelines, and data products in production environments.
Strong hands-on experience with AWS, including compute, storage, networking, IAM, security, orchestration, monitoring, and serverless or event-driven services.
Strong hands-on experience with Snowflake, including data modeling, SQL performance tuning, data pipeline integration, access control, cost optimization, data sharing, and platform governance.
Strong proficiency in Python and/or Java, with solid knowledge of software design principles, APIs, automated testing, dependency management, and application maintainability.
Experience with AWS AI services such as Amazon Bedrock, and familiarity with Generative AI, Retrieval-Augmented Generation (RAG), AI agents, and responsible AI practices is an advantage.
Experience using AI-assisted engineering tools such as GitHub Copilot, Claude, Cursor, or similar as part of daily development.
Strong understanding of Git-based workflows, CI/CD, Infrastructure as Code (IaC), DevOps practices, automated testing, and production support.
Ability to translate business requirements into technical solutions and deliver scalable, maintainable data platforms.
Strong analytical, problem-solving, communication, and stakeholder management skills.
Experience working with sensitive and confidential data while implementing governance, security, and compliance controls.
AWS certifications (Solutions Architect, Data Engineer, or Machine Learning Engineer Associate) and SnowPro® certifications are preferred.
Experience in the financial services industry is an advantage.
