Experience: 3-9 Years
Role: Data & Integration Engineer
Key Skills:
- Data Engineering,
- System Integrations,
- Python, SQL, Informatica
Responsibilities:
We are looking for a Data & Integration Engineer who operates effectively at the intersection of system analysis, enterprise integration and data engineering to support GenAI initiatives.
The successful candidate will:
- Understand business and functional requirements
- Translate them into data flows and integration designs
- Work across upstream and downstream systems
- Ensure reliable movement, transformation and availability of data for GenAI use cases
- Develop scripts / programs to get data from different integration systems
- Develop APIs to integrated with relevant systems.
The focus is on getting data into the right place, in the right format, reliably.
What Matters Most
- Strong system thinking and ability to understand end to end flows
- Ability to challenge bad designs and propose better ones
- Strong stakeholder communication
- Hands on enough to troubleshoot and implement, but not a pure coder
- Experience navigating messy enterprise ecosystems
You are not asking them to build models, just make data usable for them.
Key Responsibilities
1. System Analysis & Design
- Analyse business/technical requirements and translate them into data flows and integration designs
- Work with upstream and downstream teams to define data contracts and interfaces
- Identify gaps, inefficiencies and risks in current data movement processes
- Propose pragmatic solutions balancing speed, quality and maintainability
2. Integration & Data Movement
- Design and implement data movement across systems using:
- APIs
- SFTP and file based transfers
- Batch pipelines
- Coordinate integrations across systems in the DataLake ecosystem (Informatica, Cloudera, etc.)
- Ensure data is correctly transformed, mapped and delivered to target systems
- Troubleshoot integration issues across environments
3. Data Preparation for GenAI
- Support data ingestion and preparation for GenAI use cases:
- document ingestion
- data aggregation
- enrichment and transformation
- Work with structured and unstructured data
- Ensure data is usable for downstream AI workflows (RAG, search, investigation flows)
4. Delivery & Coordination
- Work across multiple teams:
- data platforms
- application teams
- infrastructure
- security
- Support SIT, UAT and production rollouts
- Ensure integration reliability, error handling and monitoring
- Document flows, mappings and interfaces clearly
Requirements:
Key Requirements
Below are the key skillsets that will be required for all relevant tasks mentioned:
- 5-10 years of experience in system analysis, integration engineering, data engineering or technical delivery roles.
- Strong ability to translate requirements into system flows, data flows, interface specifications and implementation plans.
- Experience working with upstream and downstream teams to define and deliver enterprise integrations.
- Practical experience with REST APIs, SFTP, batch processing, file based integration and data pipeline orchestration.
- Good understanding of data mapping, transformation, aggregation, reconciliation and data quality controls.
- Good SQL skills and basic to moderate Python skills for data handling, scripting, automation and troubleshooting.
- Exposure to Java
- Exposure to Informatica, Cloudera or similar enterprise data platforms.
- Working knowledge of Git, branching, pull requests, code reviews and controlled release practices.
- Familiarity with CI/CD, Jira, Confluence and enterprise deployment processes.
- Experience with Control M or equivalent scheduling tools.
- Familiarity with logging (OTEL) and monitoring tools such as Splunk Elastic Stack.
- Exposure to GenAI concepts such as document ingestion, RAG, embeddings and data preparation for AI workflows.
- Working experience with Informatica is preferable.
Strong communication skills, with the ability to challenge weak designs and coordinate across business, application, data, infrastructure and security teams.