Job Description
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: Bachelors 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.