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# Data Engineer

- **Company:** International Solutions Group
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Amazon Web Services, Big Data, Cloud Computing, Cloud Engineering, Encodings, Databases, Information Engineering, Data Governance, Extract Transform Load (ETL), Distributed Systems, Apache Hadoop, Hadoop Distributed File System, Apache Hive, Identity and Access Management, Python (Programming Language), Kerberos (Protocol), Performance Tuning, SQL Databases, Data Streaming, Apache Yarn, Sql Optimization, Concurrency, Apache Spark, Spring-boot, Pandas, KDB+, Containerization, Pyspark, Information Technology, Data Lineage, Influxdb, Spark Streaming, Data Pipelines, Docker
- **Published:** October 15, 2026
- **Apply:** https://www.dice.com/job-detail/a8faa84a-0d93-4633-8591-d748dfc28f93

## About the Role

* Languages: Advanced proficiency in Python (Pandas, PySpark) and Java (Spring Boot, concurrency frameworks).
 * Distributed Computing: Mastery of Apache Spark (Core, Streaming, SQL) and the Hadoop ecosystem.
 * Containerization & Cloud: Deep execution knowledge of Docker and Kubernetes/Amazon EKS cluster orchestration.
 * Databases & Storage: Advanced SQL, time-series databases (e.g., Kdb+, InfluxDB), and relational platforms.
 * Security Frameworks: Experience embedding enterprise security (Kerberos, IAM Roles, TLS encryption, KMS) into data pipelines.

Domain & Experience Qualifications

 * Experience: 5+ years of data engineering experience, preferably within investment banking, fintech, or quantitative hedge funds.
 * Financial Domain: Foundational knowledge of financial instruments, market data structures, trade lifecycles, or risk management frameworks.
 * Education: Bachelor's or Master's degree in Computer Science, Financial Engineering, or a related quantitative field.

## Description

We are seeking a Senior Data Engineer to architect and optimize our mission-critical financial data platforms. In this role, you will build ultra-reliable, high-throughput pipelines that power quantitative analysis, regulatory reporting, and risk management. You will bridge traditional enterprise financial infrastructure with modern cloud-native systems-developing in Python and Java, scaling batch and streaming workflows via Hadoop and Apache Spark, and deploying secure, containerized architectures across On-Premises systems and Amazon EKS., * Financial Pipeline Engineering: Build and maintain high-performance, deterministic ETL/ELT pipelines using Python and Java to process market feeds, transaction ledgers, and trade data.

 * Big Data & Analytics Scale: Optimize and maintain high-volume data frameworks utilizing Hadoop (HDFS, Hive, Yarn) and Apache Spark for large-scale risk modeling and backtesting.
 * Orchestration & Cloud Native: Deploy and scale containerized financial data applications and Spark streaming jobs natively inside Amazon EKS with zero-downtime tolerance.
 * Hybrid Systems Integration: Securely manage data continuity, replication, and data sovereignty between legacy, air-gapped On-Premises mainframes/servers and AWS environments.
 * Audit & Data Lineage: Implement comprehensive data governance, immutability frameworks, and end-to-end data lineage tracking to comply with financial regulations (e.g., BCBS 239, SOX, GDPR).
 * Performance Tuning: Troubleshoot complex, distributed processing jobs to minimize latency for time-sensitive financial computations and trading day closing processes.