Data Engineer - RegTech
Infosys
- Location
- Bengaluru East, Karnataka, India
- Job type
- Full-time
Required skills
- Python
- Airflow
- Azure
- CSV
- data ingestion
- data lake
- data modeling
- data models
- Databricks
- ETL
- JSON
- MySQL
- Oracle
- Parquet
- PostgreSQL
- Spark
- SQL
About the role
Infosys
Website:
infosys.com
Company:
https://www.linkedin.com/company/infosys
Industries: IT Services and IT Consulting
Job details:
- Minimum 7 years of experience in data engineering, ETL/ELT development, data warehousing, data platform engineering or related roles.
- Strong SQL skills and hands-on experience with relational databases such as PostgreSQL, SQL Server, Oracle or MySQL.
- Experience designing ingestion pipelines, transformations, validation rules, data quality checks and source-to-target mapping specifications.
- Understanding of data modeling, canonical models, metadata, lineage, audit fields, reconciliation and data quality concepts.
- Experience with Python, Spark, Databricks, Azure Data Factory, dbt, Airflow or equivalent data engineering tools is preferred.
- Ability to work with structured, semi-structured and file-based data formats such as CSV, JSON, Excel, XML and Parquet.
- Strong documentation discipline and ability to collaborate with functional SMEs and engineers on data requirements.
- Design and build data ingestion pipelines for files, APIs, SFTP sources, databases and enterprise data extracts.
- Implement canonical data models, source-to-target mapping, transformation rules, validation checks, rejected-record handling and lineage capture.
- Develop data quality checks, reconciliation routines, control result tables, exception datasets and metadata structures for auditable data processing.
- Work with PostgreSQL, SQL, object storage, data lake patterns, ETL/ELT tools and batch processing frameworks as needed.
- Partner with AI engineers to provide clean, contextual and grounded data for AI summaries, narratives, retrieval and explanations.
- Create data dictionaries, mapping specifications, interface contracts, test datasets and data-quality documentation.
- Support performance tuning, partitioning, indexing, data retention, archival and privacy-aware data handling.
- Work with QA teams to defi
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