R2R Data Engineer(R2R (Record to Report)

Summary

The R2R Data Engineer is a technical expert responsible for architecting and implementing enterprise-scale data infrastructure that powers critical financial analytics and reporting for A Finance. This role requires deep technical expertise in modern data engineering practices, including building high-performance ETL/ELT pipelines, designing scalable data models, and implementing robust data quality frameworks that ensure accuracy and consistency across financial systems.

This position demands hands-on experience with cloud-native data platforms, advanced SQL optimization, and programmatic data transformations. The engineer will work cross-functionally with business users, FDT, IS&T, data scientists, and other engineers to develop production-grade data services that support financial close processes, regulatory reporting, and strategic decision-making.

You will be working in enterprise data warehouse (Snowflake), Dataiku and lakehouse environments (AWS S3) to design dimensional models, implement data governance policies, and optimize query performance for large-scale financial datasets.

Responsibilities

• Design and implement scalable data architectures and dimensional models (star/snowflake schemas) that support financial reporting, analytics, and machine learning use cases
• Develop, test, deploy, monitor, document and troubleshoot complex data pipelines using modern orchestration frameworks with proper error handling, logging, and alerting mechanisms
• Build and maintain RESTful APIs and microservices for data access and integration with downstream applications (e.g.: Blackline)
• Implement data quality frameworks including automated validation, reconciliation logic, and anomaly detection to ensure financial data accuracy
• Optimize SQL queries and data models for performance in Snowflake, including leveraging clustering keys, materialized views, and query optimization techniques
• Design and implement secure data pipelines with end-to-end encryption, role-based access controls, and compliance with data privacy regulations
• Collaborate with data scientists and ML engineers to build feature stores and data pipelines that support machine learning model training and inference
• Establish and enforce data engineering best practices including code reviews, testing strategies (unit, integration, data quality tests), and documentation standards
• Evaluate and implement emerging technologies in the data engineering space (e.g.: streaming platforms, data quality tools, metadata management solutions)
• Participate in on-call rotation to support production data pipelines and resolve critical incidents

Key Qualifications

Required Technical Skills:

• 5+ years of advanced Python programming experience including object-oriented design, asynchronous programming, and package development
• Expert-level SQL skills including complex joins, window functions, CTEs, query optimization, and performance tuning in databases
• Hands-on experience designing and implementing data models in Snowflake including time-travel, zero-copy cloning, data sharing, and cost optimization strategies
• Proven experience building production-grade ETL/ELT pipelines processing large volumes of data
• Strong experience with AWS services including S3, Lambda, EC2, IAM, Secrets Manager, and CloudWatch
• Experience implementing data security controls including encryption at rest/in transit, data masking, tokenization, and row-level security
• Hands-on experience with CI/CD pipelines using GitHub
• Strong Git version control skills including branching strategies, pull requests, and code review processes
• Proficiency in shell scripting (Bash) for automation and system administration tasks

Preferred Technical Skills:

• Experience with streaming data platforms (Kafka, Kinesis, Pub/Sub) and real-time data processing frameworks (Spark Streaming, Flink)
• Knowledge of containerization (Docker) and orchestration platforms (Kubernetes, ECS)
• Experience with data catalog and metadata management tools (Alation, Collibra, DataHub)
• Experience with data quality frameworks (Great Expectations, Soda, Monte Carlo)
• Experience building and consuming RESTful APIs using frameworks like FastAPI or Flask

Business & Soft Skills:

• Understanding of financial processes including Record-to-Report (R2R), Order-to-Cash (O2C), Procure-to-Pay (P2P), or financial planning
• Experience working with ERP systems (SAP, Oracle Financials) and extracting data from these platforms
• Strong problem-solving skills with ability to debug complex data issues and performance bottlenecks
• Excellent communication skills with ability to explain technical concepts to non-technical stakeholders
• Experience working in Agile/Scrum environments with cross-functional teams

Education and Experience

• Bachelor''s degree in Computer Science, Computer Engineering, Data Engineering, Mathematics, Statistics, or other quantitative discipline required
• 5+ years of professional experience in data engineering roles with demonstrated expertise in building production data systems
• Master''s degree in related field preferred