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Data Engineer - PySpark & Informatica
Job Description & How to Apply Below
We are looking for a highly skilled Data Engineer with strong expertise in PySpark and Informatica Platform Engineering to support enterprise‑scale Digital Products and Digital Onboarding initiatives.
The ideal candidate should have strong experience in enterprise data engineering, big data ecosystems, data architecture, and modern data management practices within large and complex environments, preferably in banking or financial services.
Key Responsibilities- Design and develop scalable enterprise data engineering solutions using PySpark and Informatica platforms
- Build and model Raw Data Vault structures with strong understanding of:
- Source system data structures
- Data retention policies
- Data partitioning strategies
- Drive data reusability and reduce data duplication while optimizing performance
- Define and implement enterprise data architecture standards and best practices
- Create data exchange standards to support reusable and decoupled architectures
- Define standards for data archival, retention, and purging
- Identify and document critical data elements across enterprise source systems
- Collaborate with data engineers, architects, and source system SMEs to establish data engineering principles and governance standards
- Support large‑scale data transformation and analytics initiatives
- Contribute to CI/CD‑enabled data engineering and deployment environments
- Ensure compliance with enterprise data quality, governance, and security standards
- Strong hands‑on experience in:
- Py Spark
- Informatica Platform Engineering
- Enterprise Data Engineering
- Big Data Ecosystems
- Strong expertise in:
- Data Architecture
- Data Strategy & Roadmaps
- Enterprise Data Management
- Data Warehousing & Analytics Solutions
- Strong understanding of:
- Dimensional Modeling
- Star & Snowflake Schemas
- Slowly Changing Dimensions (SCD)
- Role‑playing Dimensions
- Dimensional Hierarchies
- Data Classification
- Experience transforming traditional Data Warehouse environments into Big Data‑based architectures
- Expertise in:
- Data Quality
- Data Profiling
- Data Governance
- Data Security
- Metadata Management
- Data Archival
- Experience with cloud‑native principles, architectures, and deployments
- Strong exposure to Continuous Integration (CI) and Continuous Deployment (CD) environments
- Strong analytical, troubleshooting, and problem‑solving capabilities
- Banking or financial services domain experience is highly preferred
- Experience working within enterprise‑scale digital transformation programs
- Exposure to modern analytics and enterprise data platforms
Bachelor’s or Master’s degree in Computer Science, Information Systems, Information Technology, Engineering, or related field
Preferred Candidate Profile- Ability to work in fast‑paced and evolving environments
- Strong stakeholder collaboration and communication skills
- Experience handling multiple priorities and enterprise‑scale projects
- Strong ownership mindset and attention to detail
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