Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A leading global technology company is seeking a skilled Data Applications Engineer to join its Finance Process, Analytics, Reporting & Technology (PART) Data Operations organization. The team focuses on developing next-generation workflow solutions, data applications, and AI-powered experiences that transform how finance professionals analyze data, generate insights, and make business decisions. This role offers the opportunity to work at the intersection of data engineering, software development, artificial intelligence, and business operations while delivering impactful solutions used by key stakeholders across the organization.
The organization is looking for a senior-level engineer with strong business and data expertise who is passionate about building intuitive applications that solve complex real-world challenges. The role combines data engineering, software development, applied AI, and business analysis to create innovative tools that streamline financial workflows and improve decision-making.
Key Responsibilities
- Own complex projects end-to-end, including discovery, requirements gathering, design, development, deployment, and long-term maintenance.
- Partner with financial analysts and finance leadership to understand workflows, identify pain points, and uncover opportunities for automation and AI-driven solutions.
- Design, develop, and deploy data applications, primarily using Streamlit, to automate processes, visualize data, and enhance financial operations.
- Lead the adoption of AI technologies across engineering practices, including AI-assisted development, intelligent automation, agentic tooling, and LLM-powered capabilities.
- Guide financial analysts on practical AI applications within their workflows and help prototype effective solutions.
- Write clean, maintainable, and well-documented Python code while establishing reusable development patterns and components.
- Integrate applications with databases, APIs, data lakes, and other enterprise data sources.
- Design reliable systems with strong error handling, monitoring, logging, CI/CD processes, and security best practices.
- Gather user feedback and continuously improve application usability and business impact.
- Collaborate with data scientists to integrate analytical and machine learning models into business applications.
- Establish technical standards and mentor fellow engineers.
- Document application architectures, data flows, technical decisions, and system specifications.
Required Qualifications
- 3+ years of experience in data engineering or software development with a proven track record of delivering production-grade data applications.
- Expert-level proficiency in Python and hands-on experience developing and deploying Streamlit-based applications.
- Strong experience with relational databases such as SQL Server and PostgreSQL.
- Experience working with modern data lake and lakehouse architectures.
- Solid software engineering fundamentals, including Git, code reviews, testing methodologies, CI/CD pipelines, and observability practices.
- Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.
Preferred Qualifications
- Experience building solutions with modern AI and LLM technologies, including OpenAI or Anthropic APIs, RAG architectures, agent frameworks, MCP, and prompt engineering.
- Demonstrated use of AI-assisted development tools such as Claude Code, Cursor, or GitHub Copilot.
- Experience developing Python-based web services and APIs using FastAPI, Flask, or Django.
- Familiarity with React or other modern front-end frameworks for building advanced user interfaces.
- Proficiency with data visualization tools such as Plotly, Matplotlib, and Seaborn.
- Strong understanding of data warehousing principles and ETL/ELT design patterns.
- Experience leading technical projects or mentoring engineers in a senior individual contributor capacity.
- Experience deploying LLM-powered features into production environments.
- Knowledge of cloud platforms such as AWS, Azure, or Google Cloud Platform.
- Experience with containerization and orchestration technologies including Docker and Kubernetes.
- Background supporting Finance, FP&A, or Sales Finance teams.
- Strong communication skills with the ability to bridge technical and business discussions effectively.
Disclaimer
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