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Location
In office (Bengaluru)
Seniority
Architect · 10+ years exp
Visa
Sponsors visas in United States, United Kingdom; no register of this country names it

Confirmed on the employer's own hiring board on Nov 8, 2026. First seen by Alion on Nov 5, 2026. Zensar Technologies scores A on the Alion truth index.

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Zensar Technologies is an Indian technology services company founded in 1991 and part of the RPG Group, providing digital engineering, application services and infrastructure management to clients in banking, insurance, retail, manufacturing and healthcare. Its work covers experience design, cloud and data engineering, application development and maintenance, and increasingly applied artificial intelligence, delivered from centres in India, South Africa, Europe and the United States. Headquartered in Pune and listed in India, it earns a large share of revenue from North America and has been repositioning toward higher-value engineering work.

We are seeking a Senior Data Architect, Finance Data to lead the architecture and design of Finance data platforms and data products at scale, with a primary focus on transactional finance data systems and OLTP data modeling. This role will be responsible for defining robust transactional data models, architecture patterns, governance standards, data contracts, and scalable data pipelines that support financial operations, payments, settlements, accounting, ledgers, reconciliation, reporting, and analytics. The ideal candidate brings deep expertise in OLTP-first data architecture, strong hands-on experience with MySQL and PostgreSQL, and the ability to design downstream analytical data models using Hive, Spark, and data warehouse technologies. They will partner closely with Finance, Risk, Engineering, Analytics, and Operations teams to translate business requirements into reliable, maintainable, auditable, and scalable data solutions.

Transactional Data Architecture & Modeling

  • Define and own the architecture for Finance transactional data platforms and core Finance datasets.
  • Design enterprise-grade OLTP data models using MySQL and PostgreSQL.
  • Model complex Finance domains such as payments, settlements, accounting, ledgers, reconciliation, financial events, adjustments, and reporting inputs.
  • Define table grain, primary keys, foreign keys, indexes, constraints, partitioning strategies, retention policies, and historical tracking approaches.
  • Establish standards for normalized data structures, transactional integrity, auditability, idempotency, and data consistency.
  • Design data contracts and integration patterns across Finance systems, upstream producers, and downstream consumers.
  • Ensure transactional data models are scalable, maintainable, and aligned with financial control requirements.

Analytical Data Modeling & Reporting

  • Design scalable analytical data models using Hive, Spark, and data warehouse technologies.
  • Establish standards for fact, dimension, aggregate, historical, and reporting data structures.
  • Translate transactional Finance data into reliable downstream datasets for reporting, reconciliation, analytics, and operational monitoring.
  • Define data modeling patterns that support financial reporting, business metrics, audit requirements, and analytical consumption.
  • Ensure OLAP models are consistent with source-of-truth transactional systems and Finance data contracts.
  • 10+ years of experience in Data Architecture, Data Engineering, Data Warehousing, or related fields.
  • Strong expertise in transactional data modeling and OLTP data architecture.
  • Deep hands-on experience with MySQL and PostgreSQL.
  • Strong understanding of primary keys, foreign keys, indexing, partitioning, normalization, historical tracking, auditability, and transactional integrity.
  • Experience designing data models for Finance domains such as payments, settlements, accounting, ledgers, reconciliation, financial reporting, or financial operations.
  • Strong expertise in SQL and query performance optimization.
  • Experience designing large-scale ETL/ELT architectures and data platforms.
  • Strong understanding of CDC, incremental processing, reconciliation, data quality, and metadata management.
  • Hands-on experience with analytical data platforms such as Hive, Spark, Spark SQL, or similar distributed processing technologies.
  • Strong understanding of OLAP modeling, including fact tables, dimension tables, aggregates, and reporting datasets.
  • Proven ability to drive architecture decisions and work independently as a senior technical leader
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