Engineering Lead - FSI

The Team

The Innovation & Delivery Transformation (I&DT) team is building the future of Deloitte's business through new AI-native platforms and products. The team is responsible for identifying, nurturing, scaling, and winning in new markets through new capabilities. Rather than relying on what the firm has historically done, I&DT looks ahead and invests in areas where growth is expected three, five, and ten years into the future.

Are you looking for a unique opportunity with both start-up spirit AND enterprise strength? Deloitte's Converge for FSI business offers both! We are looking for a talented Data Scientist to join the Converge for FSI team and help build differentiated, data-driven financial services products that achieve product-market fit. This is a great opportunity to work at the intersection of advanced analytics and financial services industry expertise, developing the models and insights that power Signals for FSI and future products in the Converge for FSI portfolio.

Position Summary

We are looking for a highly skilled and client-facing Engineering Lead to sit at the intersection of engineering excellence and customer partnership, supporting the Signals for FSI product. Signals is a proprietary set of data assets coupled with AI, data science, and statistical models that help our clients drive business growth and power decisions in a dynamic and changing market. You’ll own the architecture decisions that turn this from a collection of prototypes into a real platform: a shared data pipeline and warehouse that any app in the suite can draw from, a common geospatial/demographic data layer that scales nationally. In this role, you will partner with our product & data science team to integrate new data sources and harden prototypes into applications based on client feedback. This is a high-impact, high-visibility role with direct influence on product direction. You will be part of the General Manager’s leadership team, responsible for proactively bringing ideas that push the business forward, weighing in on key decisions, and helping junior resources across the team overcome hurdles. This is a build-it-for-real role for someone who’s done this exact transition before — not scaling one app, but building the reusable foundation that lets a whole suite of client-facing products scale together.

Recruiting for this role ends on 12/1/2026.

Work you’ll do

As the Engineering Lead, you will work at the intersection of data science, engineering, and financial services domain expertise to build and refine the data platforms, models, and analytics that power Signals for FSI:

• Rapidly prototype and build POCs using financial services datasets and data models that demonstrate value in the context of a client's real-world use cases
• Lead teams of onshore and offshore data engineers and application engineers to harden prototypes into production grade applications that satisfy the highly secure and regulated environments of financial services.
• Collaborate with internal Product & Data fabric teams to integrate new data sources and design and build scalable, production-ready data pipelines and applications.
• Partner with the Product team and client architects to translate financial services use cases into requirements, features, and application specifications.
• Recognize and promote opportunities for reuse when designing prototypes and applications.
• Evaluate data, model and application performance against business and regulatory considerations relevant to financial services and iterate based on findings.
• Translate analytical findings into clear, business-relevant insights for Product, Sales, and client-facing teams to shape the technical go-to-market strategy.
• Maintain a library of reusable demo assets, hardened applications, and reference implementations.
• Stay current on emerging AI/ML techniques and financial services data trends, and evaluate their application to the Signals for FSI roadmap.
• Produce high-quality technical documentation, runbooks, and integration guides.

The successful candidate would possess these skills:

• Experience in building data and insights products and pipelines that power insights and signals for business users to drive growth decisions.
• Demonstrated ability to take a working prototype and re-architect it for production scale, cost, and reliability — not just incremental feature work
• Exceptional written & verbal communicator — able to lead technical conversations with senior engineers and C-suite stakeholders alike; able to translate infrastructure tradeoffs for non-technical stakeholders
• Proven experience designing and scaling geospatial/location-based data systems (spatial indexing, PostGIS, H3, or equivalent) — ability to replace client-side point-in-radius rendering with server-side spatial queries at scale
• Strong background in ETL/data pipeline engineering — designing scheduled ingestion from third-party data vendors into a warehouse or lake (BigQuery, Snowflake, or equivalent)
• Experience with Python/FastAPI (or comparable) backend development and React/TypeScript frontend development, sufficient to review and refactor both sides of a full-stack codebase
• Hands-on experience with data security and PII handling — masking, access controls, and retention policy for datasets containing personal identifiers
• Experience standing up CI/CD pipelines and container/Helm-based deployment from scratch
• Familiarity with authentication/authorization platforms (OAuth2, enterprise SSO/gateway integration) and migrating services onto a shared platform auth layer

• Genuine interest in and working knowledge of the financial services industry, with curiosity about how data translates into real client outcomes.

• Exposure to Agentic AI frameworks, harnesses, and tooling — including LangGraph, LangChain, or similar orchestration frameworks
• Practical understanding of LLM integration patterns, prompt engineering, and AI-powered application design

• Strong analytical rigor paired with sound judgment about when a model is ready to ship versus needing further refinement.
• Comfortable moving between hands-on technical work and clear communication with product, engineering, and business stakeholders.
• Self-directed and organized, with the ability to manage multiple analytical workstreams at once.
• Collaborative mindset and a willingness to partner closely with engineering and product teams to bring models into production.
• Comfortable working through ambiguous, messy problems that require creativity and persistence.

Qualifications 

Required:

• Bachelor's degree in Engineering, Computer Science, Statistics, Data Science, Mathematics, or a related quantitative field.
• 7+ years of software engineering experience, including 3+ years in a technical lead or architecture role
• Ability to travel 10-25%, on average, based on the work you do and the clients and industries/sectors you serve.
• Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future.

Preferred:

• Advanced degree (Master’s) in Engineering, Computer Science, Statistics, Data Science, Mathematics, or a related field.
• 2+ years of experience working with financial services data (e.g., banking, capital markets, or insurance) or delivering analytics solutions to financial services clients.
• Experience with cloud-based data and analytics platforms (e.g., Snowflake, Databricks, AWS, Google Cloud, or Azure) in a financial services or other highly regulated industry context.
• Familiarity with risk, fraud, credit, or customer analytics use cases within banking, capital markets, or insurance.
• Experience with feature stores or ML feature engineering pipelines, particularly for on-demand/real-time scoring rather than static precomputed datasets
• Background in financial services or another regulated industry, with working knowledge of data vendor licensing/compliance constraints (e.g., credit bureau or B2B data providers)
• Experience with LLM/agent infrastructure (LangChain, LLM gateways) including cost monitoring and latency observability
• Familiarity with FDIC, Census, or similar public geospatial/demographic datasets
• Experience building internal developer platforms or paved-road tooling that other engineering teams consume
• Prior experience taking a product from single-region/regional pilot to national or multi-region scale
• Experience with observability stacks (e.g., Datadog, Grafana, OpenTelemetry) for both application and ML/LLM monitoring
• Background in a startup, management consulting, commercial transformation, or enterprise technology product leadership, ideally with a data focus (e.g. Epsilon, TransUnion, Equifax, Experian, Axciom)

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $141,200-$278,300.

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.