Data Engineer

Overview

Job Title: Software Development Engineer 2

Department: Technology

Location: Bangalore, India

Reporting To: Senior Research Manager - Data

Position Purpose

The Research Engineer – Data will play a pivotal role in advancing TookiTaki’s AI-driven compliance and financial crime prevention platforms through applied research, experimentation, and data innovation. This role is ideal for professionals who thrive at the intersection of research and engineering, turning cutting-edge data science concepts into production-ready capabilities that enhance TookiTaki’s competitive edge in fraud prevention, AML compliance, and data intelligence.

The role exists to bridge research and engineering by:

• Designing and executing experiments on large, complex datasets.
• Prototyping new data-driven algorithms for financial crime detection and compliance automation.
• Collaborating across product, data science, and engineering teams to transition research outcomes into scalable, real-world solutions.
• Ensuring the robustness, fairness, and explainability of AI models within TookiTaki’s compliance platform.

Key Responsibilities

Applied Research & Prototyping

• Conduct literature reviews and competitive analysis to identify innovative approaches for data processing, analytics, and model development.
• Build experimental frameworks to test hypotheses using real-world financial datasets.
• Prototype algorithms in areas such as anomaly detection, graph-based analytics, and natural language processing for compliance workflows.

Data Engineering for Research

• Develop data ingestion, transformation, and exploration pipelines to support experimentation.
• Work with structured, semi-structured, and unstructured datasets at scale.
• Ensure reproducibility and traceability of experiments.

Algorithm Evaluation & Optimization

• Evaluate research prototypes using statistical, ML, and domain-specific metrics.
• Optimize algorithms for accuracy, latency, and scalability.
• Conduct robustness, fairness, and bias evaluations on models.

Collaboration & Integration

• Partner with data scientists to transition validated research outcomes into production-ready code.
• Work closely with product managers to align research priorities with business goals.
• Collaborate with cloud engineering teams to deploy research pipelines in hybrid environments.

Documentation & Knowledge Sharing

• Document experimental designs, results, and lessons learned.
• Share best practices across engineering and data science teams to accelerate innovation.

Qualifications and Skills

Education

• Required: Bachelor’s degree in Computer Science, Data Science, Applied Mathematics, or related field.
• Preferred: Master’s or PhD in Machine Learning, Data Engineering, or a related research-intensive field.

Experience

• Minimum 4–7 years in data-centric engineering or applied research roles.
• Proven track record of developing and validating algorithms for large-scale data processing or machine learning applications.
• Experience in financial services, compliance, or fraud detection is a strong plus.

Technical Expertise

• Programming: Proficiency in Scala, Java, or Python.
• Data Processing: Experience with Spark, Hadoop, and Flink.
• ML/Research Frameworks: Hands-on with TensorFlow, PyTorch, or Scikit-learn.
• Databases: Experience with both relational (PostgreSQL, MySQL) and NoSQL databases (MongoDB, Cassandra, ElasticSearch).
• Cloud Platforms: Experience with AWS (preferred) or GCP for research and data pipelines.
• Tools: Familiarity with experiment tracking tools like MLflow or Weights & Biases.
• Application Deployment: Strong experience with CI/CD practices, Containerized Deployments through Kubernetes, Docker, etc.
• Streaming frameworks: Strong experience in creating highly performant and scalable real time streaming applications with Kafka at the core
• Data Lakehouse: Experience with one of the modern data lakehouse platforms/formats such as Apache Hudi, Iceberg, Paimon is a very strong plus.

Soft Skills

• Strong analytical and problem-solving abilities.
• Clear, concise communication skills for cross-functional collaboration.
• Adaptability in fast-paced, evolving environments.
• Curiosity-driven with a bias towards experimentation and iteration.

Key Competencies

• Innovation Mindset: Ability to explore and test novel approaches that push boundaries in data analytics.
• Collaboration: Works effectively with researchers, engineers, and business stakeholders.
• Technical Depth: Strong grasp of advanced algorithms and data engineering principles.
• Problem Solving: Dives deep into the logs, metrics and code and identifying problems, opportunities for performance tuning and optimization
• Ownership: Drives research projects from concept to prototype to production.
• Adaptability: Thrives in ambiguity and rapidly changing priorities.

Preferred

• Certifications in AWS Big Data, Apache Spark, or similar technologies.
• Experience in compliance or financial services domains.

Success Metrics

• Research to Production Conversion: % of validated research projects integrated into TookiTaki’s platform.
• Model Performance Gains: Documented improvements in accuracy, speed, or robustness from research initiatives.
• Efficiency of Research Pipelines: Reduced time from ideation to prototype completion.
• Collaboration Impact: Positive feedback from cross-functional teams on research integration.

Benefits

• Competitive Salary: Aligned with industry standards and experience.
• Professional Development: Access to training in big data, cloud computing, and data integration tools.
• Comprehensive Benefits: Health insurance and flexible working options.
• Growth Opportunities: Career progression within Tookitaki’s rapidly expanding Services Delivery team.

Introducing Tookitaki

Tookitaki: The Trust Layer for Financial Services

Tookitaki is transforming financial services by building a robust trust layer that focuses on two crucial pillars: preventing fraud to build consumer trust and combating money laundering to secure institutional trust. Our trust layer leverages collaborative intelligence and a federated AI approach, delivering powerful, AI-driven solutions for real-time fraud detection and AML (Anti-Money Laundering) compliance.

How We Build Trust: Our Unique Value Propositions

1. AFC Ecosystem – Community-Driven Financial Crime Protection: The Anti-Financial Crime (AFC) Ecosystem is a community-driven platform that continuously updates financial crime patterns with real-time intelligence from industry experts. This enables our clients to stay ahead of the latest money laundering and fraud tactics. Leading digital banks and payment platforms rely on Tookitaki to protect them against evolving financial crime threats. By joining this ecosystem, institutions benefit from the collective intelligence of top industry players, ensuring robust protection.
2. FinCense – End-to-End Compliance Platform: Our FinCense platform is a comprehensive compliance solution that covers all aspects of AML and fraud prevention from name screening and customer due diligence (CDD) to transaction monitoring and fraud detection. This ensures financial institutions not only meet regulatory requirements but also mitigate risks of non-compliance, providing the peace of mind they need as they scale.

Industry Recognition and Global Impact

Tookitaki’s innovative approach has been recognized by some of the leading financial entities in Asia. We have also earned accolades from key industry bodies such as FATF and received prestigious awards like the World Economic Forum Technology Pioneer, Forbes Asia 100 to Watch, and Chartis RiskTech100.

Serving some of the world’s most prominent banks and fintech companies, Tookitaki is continuously redefining the standards of financial crime detection and prevention, creating a safer and more trustworthy financial ecosystem for everyone.