Job Role: Data Systems Engineer – ELK / Kafka / Linux
Team: Real-Time Operations Intelligence (RTOI) – Enterprise Computing
Location: Hybrid
Alpharetta, GA or Menlo Park, CA
3 days onsite per week
Experience Level: 7–15 years
Education: Bachelor’s Degree preferred (not required)
Industry Background: Plus
Role Financial services /Banking/Investment banking
The Real-Time Operations Intelligence (RTOI) team is responsible for streaming terabytes of data daily to support enterprise-scale operational and business intelligence platforms. The team builds and supports large-scale, real-time ETL and streaming pipelines using Kafka, ELK (ElasticSearch), Snowflake, Hadoop, and Linux-based job frameworks.
This role is ideal for a hands-on Data Systems Engineer who is equally comfortable with application development, data engineering, Linux-based deployment, and production support. The engineer will work across the full development lifecycle and support hundreds of internal customers relying on real-time data systems.
Responsibilities
(Including but not limited to)
• Design, develop, deploy, and support real-time data pipelines using Kafka and ELK (ElasticSearch).
• Build and maintain large-scale ETL and streaming frameworks running on Linux platforms.
• Develop and run applications directly on Linux, including debugging CPU, memory, and performance issues.
• Support and monitor pipelines running across large-scale Kafka clusters, ensuring high availability and scalability.
• Troubleshoot and resolve production issues; ensure jobs are up and running for hundreds of internal users.
• Work with data storage and indexing in ElasticSearch, understanding how data is written, stored, and queried.
• Participate in the full software development lifecycle: requirements, design, implementation, testing, deployment, and support.
• Collaborate closely with cross-functional teams and communicate technical concepts clearly.
• Continuously learn new tools and technologies and contribute hands-on in a fast-paced environment.
Required Qualifications
• Strong, hands-on experience working on the Linux platform (development, deployment, debugging).
• 7+ years of overall professional experience in software and/or data engineering.
• Strong application development experience with:
• Python (primary)
• Ruby or Shell scripting (secondary)
• Experience building and maintaining Kafka-based data pipelines.
• Hands-on experience with ELK (ElasticSearch) for data ingestion, storage, and observability.
• Ability to understand and debug application behavior related to CPU, memory, and system performance.
• Experience working in distributed systems environments, with an understanding of scalability and trade-offs.
• Strong communication skills, team collaboration, curiosity, and willingness to “get hands dirty.”
Preferred Qualifications
• Experience with Snowflake database.
• Experience with Spark or large-scale data processing frameworks.
• Strong data analysis background.
• Experience with Flink.
• ELK / ElasticSearch certification (Observability or Data Analysis).
• Experience with cloud platforms (AWS or similar).
• Experience supporting mission-critical, real-time systems.
Technical Environment
Languages: Python, Ruby, Shell (plus Java, C/C++, or Go a plus)
Streaming & Data: Kafka, ElasticSearch (ELK), Snowflake, Hadoop
Platforms: Linux (on-prem and cloud)
Databases: SQL-based systems
Focus Areas: Real-time streaming, observability, scalability, and operational support
Interview Process
• Technical Screening (1 hour) – Focus on Linux experience and hands-on technical background
• Onsite Technical Panel – With senior team members (Ying-Yi & Yenni)
Additional Notes
• This is not a narrow or cookie-cutter data engineering role.
• Candidates must be both data engineers and application developers, not tooling-only profiles.
• The role includes development, deployment, and production support.
• Team works directly within Linux environments—deep Linux knowledge is critical.