The Role
Lead and mentor Python engineering teams while designing scalable, resilient, and secure systems. Oversee technical project delivery, architecture strategy, technology evaluation, proof-of-concept development, CI/CD, testing, monitoring, performance optimization, stakeholder communication, and risk management. The role requires hands-on development, solution architecture expertise, cloud infrastructure knowledge, API and microservices experience, and strong technical leadership.
Summary Generated by Built In
Job Title: Technical Manager
Location: Vellore, Tamil Nadu
1. Role Overview
The Technical Manager leads the engineering teams delivering and maintaining the Fixed Income data products within FactEntry / SIX.
This is a hands-on technical leadership role, not a purely administrative one. The successful candidate is expected to set and enforce engineering standards, establish development and delivery discipline, manage stakeholders across FactEntry and SIX, and build and manage the performance of the technology teams.
The role carries personal accountability for the technical integrity, delivery pace and team health of the engineering function, and acts as the primary technical escalation point at FactEntry.
2. Key Responsibilities
2.1 Technical Leadership and Standards
• Set and maintain engineering standards across the data engineering, pricing systems, UI delivery and data mining disciplines, including coding standards, code review practice, branching and release strategy, and architectural conventions.
• Ensure designs and implementations are consistent with the wider SIX BFI Unified Data Platform architecture (medallion architecture, Unity Catalog governance, Azure APIM, data pipelines) and with the platform's data governance and security requirements. In addition, all UI build should be in line with SIX UI standards.
• Define and enforce a consistent approach to testing across the team, including unit, integration and pipeline data-quality testing, UI testing, and an equivalent testing discipline for C++ pricing components (correctness, performance, and regression testing).
• Own technical decision-making within the team's remit, escalating only where decisions affect the broader platform architecture or carry material cost, schedule or risk implications.
• Maintain technical documentation standards ensuring designs, APIs, data contracts and pricing logic are documented to a standard that supports handover and audit.
• Drive effective QA practices with maximised use of testing automation to improve quality and productivity.
• Ensure all security and compliance standards are adhered to.
2.2 Delivery Discipline and Execution
• Establish and run an effective delivery cadence - sprint or short-cycle planning, backlog management, and clear definition of done - balancing pace against the rigour the financial data domain requires.
• Manage CI/CD practice across the team's outputs, including Azure DevOps pipelines.
• Track delivery progress, risks, and dependencies across the three disciplines and report status, issues and mitigations into programme governance on a regular basis.
• Identify and resolve cross-discipline dependencies - for example where pricing systems consume data engineered by the Databricks team, or where content extraction outputs feed downstream pipelines - ensuring interfaces and data contracts are agreed and respected.
• Actively manage performance improvements in data pipelines and applications.
• Drive root-cause resolution of production or pilot-environment issues rather than allowing recurring workarounds, and ensure lessons are captured and standards updated accordingly.
2.3 Pricing Systems (C++ on Azure)
• Provide technical oversight of the design and build of pricing calculation systems written in C++ and deployed on Azure, ensuring appropriate performance, accuracy and resilience for financial pricing use cases.
• Ensure pricing components are properly integrated with the wider Azure data platform - including data inputs from Databricks/Delta, exposure via Azure APIM where required, and appropriate monitoring and alerting.
• Work with the pricing engineering team to define a sound approach to numerical correctness, regression testing against reference pricing outputs, and performance benchmarking.
2.4 Content Extraction and Data Mining
• Provide technical oversight of document data mining and content extraction work, including the use of OCR and AI-based extraction tooling (e.g. Azure Document Intelligence, Mistral OCR, or equivalent) to extract structured data from unstructured source documents.
• Ensure extraction pipelines have appropriate accuracy validation, exception handling, and human-in-the-loop review processes for low-confidence extractions.
• Guide decisions on extraction tooling choice and architecture, balancing accuracy, cost, and throughput against the pilot's data volume and quality requirements.
2.5 UI Development
• Provide technical oversight of all UI development. This could be utilising React JS (and associated frameworks) or Power Platform / Power Apps for low code Ui delivery.
• Ensure automated testing frameworks are effectively used to support ongoing UI delivery.
• Work with UX designers across SIX to establish UX design standards and ensure they are adopted in the build of UIs.
2.6 Stakeholder Management
• Act as the primary FactEntry technical point of contact for the engineering function.
• Translate business and architectural requirements into clear technical work for the team, and translate technical constraints, trade-offs and risks back into terms stakeholders can act on.
• Represent the engineering function in applicable governance forums
2.7 Team Building and Performance Management
• Build and shape a team spanning internal hires and potentially supplier resources including input to resourcing decisions, role definition, and onboarding.
• Mentor developers to drive highly productive engineering practices.
• Identify skills gaps across the team - including any cross-discipline gaps between data engineering, C++ pricing, and content extraction - and put in place training, mentoring, or resourcing plans to address them.
• Foster a collaborative working culture across what is a multi-disciplinary, multi-supplier team, and manage performance issues directly and promptly where they arise.
• Support recruitment and supplier resourcing processes, including technical input to candidate assessment and interview panels.
3. Required Skills & Experience
• Significant experience leading multi-disciplinary engineering teams, ideally including both data engineering and preferably a second technical discipline such as building and maintaining performance critical systems such as pricing and analytics.
• Strong working knowledge of modern Azure-based data platforms, including Databricks, Delta Lake / medallion architecture, ADF, Unity Catalog, and Azure APIM, sufficient to set standards and make architectural judgement calls without relying solely on individual contributors.
• Experience managing teams building UIs in tools such as ReactJS (and associated frameworks) and PowerPlatform.
• Familiarity with C++ / Java development practices and the engineering discipline required for performance-critical or pricing-sensitive systems (memory management, numerical precision, latency, testing rigour).
• Proven track record of establishing engineering standards, CI/CD discipline, and delivery cadence in a fast-paced or pilot/proof-of-concept environment.
• Strong stakeholder management skills, with experience operating credibly with senior business and architecture stakeholders as well as supplier organisations.
• Demonstrated experience in team building, performance management, and developing engineers across a mixed internal/supplier team structure.
• Experience working within the financial services or financial information sector, ideally with exposure to fixed income, reference data, or market data domains.
4. Desirable / Nice-to-Have
• Direct prior experience managing or working closely with quantitative or pricing engineering teams in a financial services context.
• Understanding of OCR and AI-based content/document extraction approaches and the practical challenges of accuracy, exception handling, and human review in production extraction pipelines.
• Familiarity with SAFe or other scaled agile delivery frameworks, and the ability to adapt them pragmatically for a pilot-scale team.
• Experience managing distributed or partially outsourced engineering teams across multiple supplier organisations.
Skills Required
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field
- 7-10 years of software development experience
- At least 3 years in a managerial or technical leadership role
- At least 2 years of solution architecture experience
- Expertise in Python and Python frameworks such as Django, Flask, or FastAPI
- Strong understanding of object-oriented programming, design patterns, and software development best practices
- Strong knowledge of system design and architecture patterns, including microservices and event-driven architecture
- Experience with cloud infrastructure, including AWS, GCP, or Azure
- Experience with relational and NoSQL databases, distributed systems, and large-scale web applications
- Strong familiarity with DevOps practices, CI/CD pipelines, containerization, Docker, Kubernetes, and cloud deployment strategies
- Extensive experience designing RESTful APIs and microservices architectures
- Proven experience managing and mentoring engineering teams
- Strong analytical problem-solving skills
- Excellent verbal and written communication skills
- Extensive knowledge of Python frameworks
- Experience in Agile or Scrum environments
- Knowledge of data science libraries such as Pandas and NumPy
- Knowledge of machine learning frameworks such as TensorFlow and PyTorch
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The Company
What We Do
FactEntry is a London-headquartered provider of fixed-income reference data, bond pricing, analytics, and related data solutions for debt-capital-markets professionals. Its products include a validated securities master and reference database, bond valuation engine, corporate-actions tracking, bond-document database, municipal disclosures, and regulatory data services. The company combines analyst-led research with machine learning and natural-language-processing technologies to deliver reliable market data and monitoring.
