Field Engineer

Work directly with clients, virtually or on-site, to design, develop, and deploy custom integrations, automations, and data pipelines using APIs, Databricks, and modern engineering tools.

Build production-ready solutions that accelerate client onboarding, reduce time-to-value, and support operational scalability.

Develop intelligent automation using AI, large language models (LLMs), and machine learning to address complex data transformation, validation, and reconciliation challenges.

Lead Databricks-based implementations, designing reliable data infrastructure to streamline onboarding and analytics.

Translate complex or ambiguous client requirements into scalable technical solutions through discovery, prototyping, and iterative development.

Write high-quality Python code for ETL pipelines, API integrations, data validation frameworks, and automation tools.

Own technical workstreams from requirements gathering and solution design through production deployment and ongoing client success.

Collaborate with Product, Engineering, and Services teams to identify opportunities for product improvements and contribute to the platform roadmap.

Develop internal tools, reusable frameworks, and best practices to improve delivery efficiency across the team.

Set and communicate technical expectations clearly with client stakeholders and internal teams.

Manage multiple client engagements, prioritize competing demands, and ensure projects reach successful completion.

Act as a trusted technical advisor, proactively identifying opportunities to deliver additional value to clients.

At least 3 years of experience in software engineering, data engineering, or forward-deployed technical roles.

Advanced proficiency in Python, including experience developing production-grade applications, APIs, and data pipelines.

Strong understanding of REST APIs, authentication mechanisms, and integration architecture.

Practical experience with Databricks, Spark, and distributed data processing frameworks.

Proficiency in SQL and experience managing large datasets across relational and NoSQL databases.

Experience designing and implementing ETL/ELT pipelines for complex data transformations.

Familiarity with AI and machine learning tools, including the implementation of LLM-powered automation solutions.

Understanding of financial data domains, such as portfolio management, performance analytics, and multi-asset class portfolios.

Experience with Git, CI/CD pipelines, and modern software development practices.

Strong problem-solving skills, a proactive mindset, and the ability to turn complex challenges into practical solutions.

Excellent communication and interpersonal skills, particularly when explaining technical concepts to non-technical audiences.

Ability to thrive in fast-paced and ambiguous environments while maintaining high standards of quality.

Independent, adaptable, and entrepreneurial approach, with a strong sense of ownership and commitment to client outcomes.

Collaborative attitude, strong work ethic, and willingness to contribute to team success.

Opportunity to work on advanced financial data solutions and AI-powered technology.

Direct exposure to complex client challenges and opportunities to deliver measurable business impact.

Professional growth through collaboration with experienced Product, Engineering, and Services teams.

Opportunities to influence product development, technical roadmaps, and engineering best practices.

A collaborative environment that encourages innovation, continuous learning, shared ownership, and client-focused delivery.