Job Requirements Bachelor's or Master's degree in
Computer Science, Data Engineering, Information Systems
, or a related field. Atleast
6-10 years of experience in data engineering
, with at least
3 years in the BFSI domain
(banking, insurance, or capital markets). Proven experience in building real-time and batch data pipelines on
Azure or AWS ecosystems. Strong proficiency in Python, PySpark, SQL, and Scala for data transformation, processing, and automation. Hands-on experience working with modern data platforms such as
Azure Data Lake, Azure Synapse Analytics, Databricks, and Snowflake for scalable dat a engineering solutions. Proven ability to design, schedule, and monitor data workflows using orchestration tools like
Apache Airflow, Azure Data Factory, and dbt. Experience in developing and maintaining real-time data streaming pipelines leveraging
Kafka, Confluent, or Azure Event Hubs. Familiarity with data governance frameworks and tools such as Apache Atlas, Azure
Purview, and Collibra to ensure data lineage, cataloging, and compliance. Strong understanding of data security practices
, including encryption, role-based access control (RBAC), tokenization, and audit logging, aligned with regulatory and compliance standards. Practical knowledge of CI/CD and Infrastructure as Code (IaC) tools - Terraform, Azure DevOps, and GitHub Actions - for automating data infrastructure deployment and management
. Exposure to regulatory data models such as
MAS 610, Basel III, IFRS 9/17, BCBS 239
. Experience integrating
DevOps and MLOps practices into data engineering workflows. Strong analytical and problem-solving mindset with attention to detail. Ability to collaborate effectively with cross-functional and geographically distributed teams
. Excellent written and verbal communication skills. Demonstrated ownership, accountability
, and commitment to quality and delivery
. Highly Preferred certifications: Microsoft
Azure Data Engineer Associate Databricks Data Engineer Professional Snowflake SnowPro Core
senior
4/16/2026
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