Enterprise Solutions Architect

Job Description

Role : Enterprise Architect

Who are we looking for?

As a Senior Data Architect in the BFSI domain, you will be responsible for designing and governing data architecture across core banking, Wealth Management and financial platforms. You’ll lead initiatives that span legacy systems (mainframes) to modern cloud-native and NoSQL solutions, ensuring regulatory compliance, data lineage, and high availability for mission-critical applications.

Domain Expertise :

Deep understanding of

BFSI data domains : customer, account, transaction, trading, portfolios, positions, credit, risk, claims, policy, and underwriting.

Experience with

financial data modeling , including

ledger structures ,

risk hierarchies , and

regulatory taxonomies .

Data Modeling & Architecture :

Proficiency in

Erwin ,

PowerDesigners , or

Toad Data Modeler .

Experience with

3NF ,

dimensional modeling ,

data vault , and

canonical models .

Key Responsibilities :

Architect scalable data solutions for

core banking ,

Wealth Management ,

risk management ,

fraud detection , and

regulatory reporting .

Design

data models

for transactional systems, data warehouses, and real-time analytics platforms.

Lead

data migration

from legacy systems (e.g., DB2, VSAM) to cloud platforms like

Snowflake ,

Azure Synapse , or

Google Big Query.

Lead Data migration from Oracle to Postgres, SQL Server to Postgres

Collaborate with compliance teams to ensure

data governance ,

auditability , and

regulatory alignment

(e.g., Basel III, IFRS, GDPR).

Build and optimize

ETL / ELT pipelines

for high-volume financial data using tools like

Informatica ,

Talend ,

Apache NiFi , or

Azure Data Factory .

Integrate data across

core banking systems ,

Wealth Management Systems ,

CRM ,

insurance platforms , and

digital channels .

Define metadata standards, lineage tracking, and master data management frameworks.

Mentor data engineers and analysts; contribute to architectural reviews and BFSI-specific best practices.

Knowledge & Skills :

Databases & Platforms :

Category Technologies

Mainframe -

IBM DB2, IMS, VSAM

Relational -

Oracle, SQL Server, PostgreSQL,

NoSQL -

MongoDB, Cassandra, Couchbase, Aerospike

Cloud DBs -

Snowflake, Redshift, Big Query, Azure Synapse,

Yugabyte

Data Lakes - Hadoop, Azure Data Lake, Amazon S3

Tools - SQL server Management Studio, TOAD, Foglight, Boost, Studio 3T

ETL & Streaming :

Informatica , Talend, Apache NiFi, Azure Data Factory, IICS,

Airflow and DBT

Kafka , Spark Streaming, Flink for real-time data ingestion

Cloud & DevOps : Hands-on with

GCP

data services

AWS ,

Azure

Familiarity with

Terraform ,

Git ,

CI / CD ,

Docker ,

Kubernetes

Governance & Security :

Tools : Collibra, Alation, Apache Atlas

Practices : Data lineage, masking, encryption, RBAC, audit trails

Programming & Scripting :

SQL, Python, Shell scripting, Scala (optional)