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Bengaluru, Karnataka, India
Remote only
Not Available
This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Senior Data Engineer based in India.
We are looking for an experienced Senior Data Engineer to build and scale reliable data infrastructure within an AI-driven financial technology environment. In this role, you will design high-performance data pipelines, develop robust data models, and integrate information from diverse internal and external sources. Your work will support critical workflows across lending operations, intelligent document processing, underwriting, analytics, and automated borrower interactions. You will use AI-powered development tools and emerging automation techniques to accelerate engineering workflows, improve data quality, and optimize performance. Working closely with product managers, data scientists, analysts, and software engineers, you will help transform complex datasets into trusted, actionable information. This is an exciting opportunity for a hands-on engineer who thrives in a fast-paced environment and wants to shape the future of AI-powered data systems.
Design and develop data pipelines: Build, maintain, and optimize scalable ETL/ELT pipelines that process structured and unstructured data from internal platforms, third-party providers, APIs, and external business systems.
Build data warehouses and models: Develop and optimize data warehouses and data marts using technologies such as Snowflake, BigQuery, or equivalent platforms. Design conceptual, logical, and physical data models that provide consistent, reliable datasets for analytics, reporting, and product applications.
Manage data ingestion and integrations: Create and maintain robust ingestion frameworks and API connectors for CRM platforms, financial data services, SaaS applications, and internal systems. Ensure integrations handle schema changes, rate limits, failures, and recovery effectively.
Implement data quality and governance: Establish validation rules, schema management practices, data documentation, and quality controls to maintain data accuracy, consistency, and traceability. Use AI-assisted profiling, anomaly detection, and automated documentation to strengthen data governance.
Integrate AI into engineering workflows: Regularly use AI-powered coding assistants, intelligent autocomplete, code generation, and automated testing tools to improve development speed and quality. Evaluate emerging AI tools and incorporate effective practices into the team's engineering processes.
Develop intelligent and agentic workflows: Build and support multi-step automated processes that interact with data in real time, including AI-powered validation, enrichment, and other intelligent data operations.
Optimize performance and reliability: Monitor and tune data pipelines, warehouse queries, and processing jobs for scalability, efficiency, and cost optimization. Implement logging, monitoring, and alerting to identify failures quickly and support timely incident resolution.
Apply security and compliance controls: Implement data privacy, security, and governance measures appropriate for financial services, ensuring that data access and transformations are appropriately controlled and traceable.
Enable analytics and product development: Deliver clean, consistent, and well-structured datasets that help analysts, product managers, and operational teams make informed decisions. Collaborate with data scientists and full-stack engineers to align data architecture with evolving product and business requirements.
Improve engineering practices: Contribute to production-grade development standards, CI/CD workflows, automated testing, documentation, and continuous improvement across the data platform.
Professional experience: At least 5 years of experience in data engineering or a related backend, data-focused engineering role, with a proven track record of delivering production-grade data pipelines at scale.
Programming and data transformation: Strong SQL and Python skills, including the ability to develop reliable transformation logic, automate data workflows, and troubleshoot complex processing issues.
ETL/ELT frameworks: Hands-on experience with modern data engineering frameworks and tools, such as dbt, for building, testing, and maintaining data transformation workflows.
Cloud and data warehouse technologies: Proficiency with cloud platforms, preferably AWS, and experience with serverless data services. Strong familiarity with modern data warehouse technologies, ideally Snowflake, is expected.
Data modeling expertise: Experience designing scalable data models using approaches such as Kimball dimensional modeling, Star Schema, or Data Vault, with a focus on consistency, maintainability, and downstream usability.
API integration and ingestion: Demonstrated ability to integrate third-party APIs and external data sources, develop reliable ingestion mechanisms, and manage schema evolution, rate limits, and error handling.
AI-assisted development: Regular practical use of AI-powered engineering tools such as Cursor, GitHub Copilot, Claude Code, or similar solutions to accelerate coding, pipeline development, debugging, testing, or documentation.
Production engineering and CI/CD: Experience building and operating reliable data systems in production, implementing CI/CD practices for data workflows, and applying monitoring, testing, and performance optimization techniques.
Cross-functional collaboration: Ability to work closely with product managers, data scientists, analysts, and full-stack engineers to translate business requirements into effective data solutions.
Startup mindset: A hands-on, resourceful, and adaptable approach, with the ability to work independently, manage changing priorities, and deliver results in a fast-paced environment.
Agentic AI and automation experience: Familiarity with agentic workflows, real-time automated processes, and the data infrastructure needed to support AI-powered tools and intelligent automation platforms is an advantage.
Financial data and compliance knowledge: Experience working with financial datasets and APIs in regulated or high-compliance environments is preferred. Understanding of data privacy regulations such as GDPR and CCPA is also beneficial.
Prompt engineering and AI workflows: Experience using prompt engineering for code generation, data transformation, or AI-powered data workflows is an additional advantage.
Competitive compensation: Receive a competitive salary, with specific compensation details to be confirmed during the hiring process.
Flexible paid time off: Benefit from an unlimited paid time off policy, subject to applicable company guidelines.
Remote-first work environment: Work remotely with flexible working hours, supporting greater autonomy and work-life balance.
Professional development budget: Access up to $2,000 per year for professional development, including relevant learning opportunities and skill-building activities.
Home office support: Receive a home office setup stipend to help create a productive and comfortable remote workspace.
AI-focused innovation: Work with modern data engineering technologies and AI-assisted development practices while contributing to intelligent, automated financial workflows.
Cross-functional collaboration: Partner with engineering, product, and data specialists to build scalable systems with a direct impact on business operations and decision-making.