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
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