Data Engineer - C11 - Chennai
Content + Source + Freshness • 14 Dec 2025 • 95% confidence
Offer value
Valuable role in data engineering with solid advancement potential within a reputable banking institution.
- Engage in data engineering at a reputable banking institution
- Opportunity to learn and grow in data infrastructure
- Solid balance of technical and analytical skills required
- Requires depth of experience and significant technical expertise
Pros
- Engagement in high-impact data projects with regulatory implications
- Opportunity to work in a strong team environment
- Learning potential through complex data engineering challenges
Cons
- Requires specialized technical knowledge and programming skills
- May involve long hours during high-pressure periods
- Potentially limited visibility in a large organizational structure
Who it's for
Mid-level / Experienced Professional • Full-time / Primarily Office-based
Good fit
- Data engineers with several years of experience
- Professionals seeking stable careers in finance
- Candidates passionate about data solutions and compliance
Not recommended for
- Inexperienced individuals in technical roles
- Candidates not comfortable with structured work environments
- Those preferring less technical data responsibilities
Motivation fit
Key skills
About the job
The Role
The Data Engineer is accountable for developing high quality data products to support the Bank’s regulatory requirements and data driven decision making. A Data Engineer will serve as an example to other team members, work closely with customers, and remove or escalate roadblocks. By applying their knowledge of data architecture standards, data warehousing, data structures, and business intelligence they will contribute to business outcomes on an agile team.
Responsibilities
- Developing and supporting scalable, extensible, and highly available data solutions
- Deliver on critical business priorities while ensuring alignment with the wider architectural vision
- Identify and help address potential risks in the data supply chain
- Follow and contribute to technical standards
- Design and develop analytical data models
Required Qualifications & Work Experience
- First Class Degree in Engineering/Technology (4-year graduate course)
- 5 to 8 years’ experience implementing data-intensive solutions using agile methodologies
- Experience of relational databases and using SQL for data querying, transformation and manipulation
- Experience of modelling data for analytical consumers
- Ability to automate and streamline the build, test and deployment of data pipelines
- Experience in cloud native technologies and patterns
- A passion for learning new technologies, and a desire for personal growth, through self-study, formal classes, or on-the-job training
- Excellent communication and problem-solving skills
Technical Skills (Must Have)
- ETL: Hands on experience of building data pipelines. Proficiency in two or more data integration platforms such as Ab Initio, Apache Spark, Talend and Informatica
- Big Data: Experience of ‘big data’ platforms such as Hadoop, Hive or Snowflake for data storage and processing
- Data Warehousing & Database Management: Understanding of Data Warehousing concepts, Relational (Oracle, MSSQL, MySQL) and NoSQL (MongoDB, DynamoDB) database design
- Data Modeling & Design: Good exposure to data modeling techniques; design, optimization and maintenance of data models and data structures
- Languages: Proficient in one or more programming languages commonly used in data engineering such as Python, Java or Scala
- DevOps: Exposure to concepts and enablers - CI/CD platforms, version control, automated quality control management
Technical Skills (Valuable)
- Ab Initio: Experience developing Co>Op graphs; ability to tune for performance. Demonstrable knowledge across full suite of Ab Initio toolsets e.g., GDE, Express>IT, Data Profiler and Conduct>IT, Control>Center, Continuous>Flows
- Cloud: Good exposure to public cloud data platforms such as S3, Snowflake, Redshift, Databricks, BigQuery, etc. Demonstratable understanding of underlying architectures and trade-offs
- Data Quality & Controls: Exposure to data validation, cleansing, enrichment and data controls
- Containerization: Fair understanding of containerization platforms like Docker, Kubernetes
- File Formats: Exposure in working on Event/File/Table Formats such as Avro, Parquet, Protobuf, Iceberg, Delta
- Others: Basics of Job scheduler like Autosys. Basics of Entitlement management
- Certification on any of the above topics would be an advantage.
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Job Family Group:
Technology------------------------------------------------------
Job Family:
Digital Software Engineering------------------------------------------------------
Time Type:
Full time------------------------------------------------------
Most Relevant Skills
Please see the requirements listed above.------------------------------------------------------
Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.------------------------------------------------------
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