Data & Integration Engineer (GenAI & Enterprise Data Integration) - Banking

Job Summary

We are seeking an experienced Data & Integration Engineer to join our enterprise data team supporting next-generation Generative AI (GenAI) initiatives. This role sits at the intersection of system analysis, enterprise integration, and data engineering, ensuring seamless movement, transformation, and availability of enterprise data across multiple platforms.

The ideal candidate will understand business requirements, translate them into scalable integration solutions, and work closely with application, infrastructure, security, and data platform teams to deliver reliable, high-quality data pipelines that enable AI-powered business solutions. This is an excellent opportunity for professionals who enjoy solving complex enterprise integration challenges and working across diverse technology ecosystems.

Key Responsibilities1. System Analysis & Solution Design

Analyze business and technical requirements and convert them into end-to-end system flows, data flows, and integration designs.

Collaborate with business stakeholders and technical teams to define interface specifications and data contracts.

Evaluate existing integration processes, identify gaps, inefficiencies, and risks.

Recommend scalable, maintainable, and cost-effective integration solutions.

Create technical documentation including architecture diagrams, interface mappings, and process documentation.

2. Enterprise Integration & Data Engineering

Design, develop, and maintain enterprise data integrations using:

REST APIs

SFTP/File-based integrations

Batch processing

Enterprise data pipelines

Develop scripts and automation programs to retrieve and process data from multiple enterprise systems.

Build APIs to integrate applications and enterprise platforms.

Coordinate data movement across enterprise Data Lake environments including Informatica, Cloudera, and related platforms.

Ensure accurate data transformation, mapping, reconciliation, validation, and delivery.

Troubleshoot integration failures and resolve production issues across multiple environments.

3. Data Preparation for Generative AI

Support enterprise GenAI initiatives by preparing high-quality datasets for AI applications.

Design and implement:

Document ingestion pipelines

Data aggregation processes

Data enrichment and transformation workflows

Work with both structured and unstructured data sources.

Prepare data for downstream AI use cases including:

Retrieval-Augmented Generation (RAG)

Semantic Search

Investigation workflows

Enterprise knowledge retrieval

Ensure data quality, consistency, and readiness for AI consumption.

4. Delivery & Cross-functional Collaboration

Work closely with:

Data Engineering teams

Application Development teams

Infrastructure teams

Security teams

Business stakeholders

Participate in SIT, UAT, deployment, and production support activities.

Implement monitoring, logging, scheduling, and error handling mechanisms.

Support controlled releases following enterprise DevOps practices.

Document integration flows, mappings, APIs, and operational procedures.

Required Skills & Qualifications

Bachelor's Degree in Computer Science, Information Technology, Engineering, or a related discipline.

5–10 years of experience in:

Data Engineering

System Integration

Enterprise Application Integration

Technical Delivery

Strong understanding of enterprise system architecture and end-to-end integration design.

Proven experience translating business requirements into technical implementation plans.

Experience working with upstream and downstream enterprise systems.

Technical Skills

Strong SQL skills for querying, validation, reconciliation, and troubleshooting.

Basic to intermediate Python programming for scripting, automation, and data processing.

Exposure to Java development.

Hands-on experience with:

REST APIs

SFTP

File-based integrations

Batch processing

Enterprise data pipelines

Experience with:

Informatica (Preferred)

Cloudera or similar enterprise data platforms

Working knowledge of:

Git

Branching strategies

Pull Requests

Code Reviews

Familiarity with:

CI/CD pipelines

Jira

Confluence

Enterprise release processes

Experience with Control-M or equivalent scheduling tools.

Familiarity with monitoring and observability tools including:

Splunk

Elastic Stack

OpenTelemetry (OTEL)

GenAI Knowledge

Exposure to enterprise AI concepts including:

Document ingestion

Retrieval-Augmented Generation (RAG)

Embeddings

AI data preparation

Enterprise search

Knowledge retrieval workflows

Preferred Skills

Experience with Informatica Data Integration.

Exposure to enterprise Data Lake architectures.

Experience supporting cloud-based data platforms.

Understanding of enterprise security and governance standards.

Knowledge of AI/ML data pipelines and modern data architectures.

Soft Skills

Excellent analytical and problem-solving abilities.

Strong communication and stakeholder management skills.

Ability to challenge existing designs and recommend improved solutions.

Strong system thinking with an end-to-end enterprise perspective.

Ability to work effectively across cross-functional teams.

Hands-on approach to troubleshooting while maintaining architectural oversight.

Comfortable working in complex enterprise environments with multiple stakeholders.