Description

Responsibilities

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.

Data Pipeline Architecture

● Architect and guide the implementation of ETL/ELT pipelines supporting Finance use

cases.

● Design scalable ingestion and processing patterns, including batch, incremental, CDC,

backfill, reconciliation, and recovery workflows.

● Ensure reliability, scalability, observability, and maintainability of Finance data pipelines.

● Drive architecture decisions for performance optimization, operational resilience, and

cost efficiency.

● Define pipeline patterns that preserve transactional accuracy while enabling downstream

analytics and reporting.

Governance, Quality & Controls

● Establish data quality, lineage, metadata, ownership, and governance standards for

Finance data.

● Define reconciliation frameworks and controls to ensure financial data accuracy,

completeness, and auditability.

● Drive best practices for monitoring, validation, anomaly detection, and operational

excellence.

● Partner with stakeholders to ensure compliance with financial reporting, audit, and

control requirements.

Technical Leadership

● Provide architectural guidance and design reviews across Finance data initiatives.

● Mentor engineers and data practitioners on OLTP modeling, Finance data architecture,

and downstream analytical modeling best practices.

● Collaborate with Finance, Risk, Engineering, Analytics, and Operations teams to align

business and technical goals.

● Influence long-term Finance data platform strategy, architecture standards, and

roadmap.

Required Qualifications

● 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.

Preferred Qualifications

● Experience with Spark SQL, PySpark, Hive SQL, Airflow, or similar orchestration

frameworks.

● Experience designing Finance data architectures in fintech, payments, marketplace,

mobility, or large-scale technology environments.

● Exposure to data governance, cataloging, lineage, and observability platforms.

● Experience supporting audit, compliance, reconciliation, and financial controls

requirements.

● Prior experience working with large-scale transactional Finance systems is a strong plus

Key Skills

Education

Bachelor's degree