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Remote Hedge Fund Data Engineer Jobs in Georgia (NOW HIRING)

Sequoia Connect

Senior AWS Data Engineer

Atlanta, GA · Remote

$101K - $138K/yr

Remote If you meet these qualifications and are pursuing new challenges, start your application on ... data integration patterns. * Familiarity with GitLab, Terraform, CI/CD, and AWS developer tools.

PrizePicks

Data Platform Engineer

Atlanta, GA · Remote

$145K - $175K/yr

As a Data Platform Engineer, you will contribute to building the modern Data platform at Prizepicks ... S. and are willing to consider remote candidates. #LI-Remote Working at PrizePicks: The typical ...

Bravo17

Lead Systems Engineer (data)

Atlanta, GA · On-site +1

See us at Position Summary As a Lead Systems Engineer - Data at Bravo17, you will elevate data ... Remote; however we are looking to grow our presence in Atlanta, Chicago, Denver, and Washington DC ...

Federal Reserve Bank of Richmond

Data Architect

Atlanta, GA · On-site +1

$61.25 - $78.75/hr

Enable advanced analytical capabilities for researchers and developers. * Partner with the ... No * Current remote employees will not be guaranteed to keep their remote status with their ...

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Remote Hedge Fund Data Engineer information

What does a remote hedge fund data engineer do?

A Remote Hedge Fund Data Engineer is responsible for designing, building, and maintaining data pipelines and infrastructure that support the data needs of a hedge fund, all while working from a remote location. They collect, process, and analyze large volumes of financial and market data to enable investment strategies and decision-making. This role often involves working with modern data technologies, ensuring data quality, and collaborating with portfolio managers, analysts, and other engineers. Remote Data Engineers must also ensure secure and efficient data flow, troubleshoot issues, and optimize data systems for performance.

What are the key skills and qualifications needed to thrive as a remote hedge fund data engineer?

To thrive as a Remote Hedge Fund Data Engineer, you need strong programming skills (typically in Python, SQL, or Java), a solid understanding of financial data structures, and a degree in computer science, engineering, or a related field. Expertise in cloud platforms (such as AWS or Azure), data pipeline tools (like Apache Spark or Airflow), and experience with big data technologies are commonly required, along with relevant certifications. Exceptional analytical thinking, attention to detail, and clear communication help you proactively solve complex problems and collaborate effectively with distributed teams. These skills are crucial for ensuring the timely, accurate, and secure processing of large-scale financial data that drives investment decisions.

How does a remote hedge fund data engineer typically collaborate with portfolio managers and quantitative analysts?

As a Remote Hedge Fund Data Engineer, you will frequently work alongside portfolio managers and quantitative analysts to ensure access to timely, high-quality data for investment decision-making. Collaboration often involves gathering requirements for new data sources, supporting the development and maintenance of data pipelines, and troubleshooting data quality issues. Effective communication is essential, as you'll need to translate business needs into technical solutions and provide ongoing support for data-driven strategies, all while working within a distributed team environment. Regular virtual meetings, shared documentation, and version control tools help maintain alignment and foster a collaborative workflow.

What is the difference between Remote Hedge Fund Data Engineer vs Remote Quantitative Analyst?

AspectRemote Hedge Fund Data EngineerRemote Quantitative Analyst
Required CredentialsBachelor's in Computer Science, Data Science, or related field; experience with data engineering toolsBachelor's or higher in Mathematics, Statistics, or related; programming skills in Python, R
Work EnvironmentFinancial firms, hedge funds, or asset management companies; focus on data pipelinesResearch firms, hedge funds, or investment banks; focus on modeling and analysis
Employer & Industry UsageCommonly employed in hedge funds for data infrastructureUsed for developing trading strategies and risk models in finance

The main difference is that Remote Hedge Fund Data Engineers focus on building and maintaining data systems, while Remote Quantitative Analysts develop models and strategies. Both roles require strong analytical skills, but their daily tasks and focus areas differ within the hedge fund industry.

What are popular job titles related to Remote Hedge Fund Data Engineer jobs in Georgia?

For Remote Hedge Fund Data Engineer jobs in Georgia, the most frequently searched job titles are:

What job categories do people searching Remote Hedge Fund Data Engineer jobs in Georgia look for?

The top searched job categories for Remote Hedge Fund Data Engineer jobs in Georgia are:

What cities in Georgia are hiring for Remote Hedge Fund Data Engineer jobs?

Cities in Georgia with the most Remote Hedge Fund Data Engineer job openings:

Remote Hedge Fund Data Engineer jobs near you
Infographic showing various Remote Hedge Fund Data Engineer job openings in Georgia as of August 2026, with employment types broken down into 1% As Needed, 86% Full Time, 11% Part Time, and 2% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution.

Senior AWS Data Engineer

Sequoia Connect

Atlanta, GA • Remote

$101K - $138K/yr

Full-time

Posted 23 days ago

Job description

Description
At Sequoia Connect, we are a Talent-First Technology Ecosystem that redefines how elite professionals interact with the global digital landscape. We move beyond traditional models to act as a catalyst for the top 1% of global talent, connecting human potential with complex industrial execution. By joining our inner circle, you are not simply taking a position; you are aligning with a strategic partner dedicated to updating your "Human OS" and accelerating your growth through world-class, high-impact projects.
We are currently partnering with a rapidly growing, automation-led powerhouse that serves 31 Fortune 500 companies across the financial, healthcare, and manufacturing sectors. With a global workforce of over 32,000 employees and a presence in 28 countries, our client is a titan of digital transformation. Their "Automate Everything, Cloudify Everything" strategy ensures you will be working at the absolute forefront of AI-driven automation and cloud solutions.
This is your chance to thrive in a "Customer Success, First and Always" environment that prizes continuous learning and radical ownership. You will collaborate within an international network of expertise across 39 delivery centers worldwide, gaining exposure to complex engineering challenges that redefine industrial standards. If you are a driven professional looking for a dynamic, forward-thinking workplace where your growth is the priority, this is where you belong.
We are currently searching for a Senior AWS Data Engineer:
The Challenge (Responsibilities)
  • Design, build, and maintain scalable ETL and data pipelines using Python, PySpark, and AWS services.
  • Develop and manage workflows using Glue, Lambda, and Step Functions.
  • Build event-driven integrations using SNS, SQS, and related AWS services.
  • Design and optimize data lake and warehouse solutions using S3, Athena, and Redshift.
  • Write and tune high-performance SQL for complex transformations and reporting.
  • Design and Implement data validation, monitoring, logging, alerting, and error-handling frameworks.
  • Design and Support API-based and JSON-based integrations across internal and external systems.
  • Troubleshoot operational issues, identify root causes, and implement corrective actions.
  • Contribute to documentation, deployment automation, and engineering best practices.
  • Collaborate with stakeholders and mentor junior team members where needed.

Your Profile (Requirements)
  • Degree holders for the visa application process.
  • 8-12 years of software development experience across the appropriate platform.
  • Solid IT background and experience.
  • Experience as an application developer for projects similar in scope and responsibility.
  • Strong expertise in Python, PySpark, and SQL.
  • Strong hands-on experience with AWS services such as Glue, Redshift, RDS, Lambda, Step Functions, SNS, SQS, S3, Athena, EMR, CloudWatch, and CloudTrail.
  • Experience with enterprise data lakes, data warehouses, data marts, and big data environments.
  • Strong understanding of ETL best practices, data quality, and operational support.
  • Experience with APIs, JSON, and cloud-based data integration patterns.
  • Familiarity with GitLab, Terraform, CI/CD, and AWS developer tools.
  • Experience with DynamoDB, EC2, ECS and Batch.
  • Exposure to data migration and cloud modernization programs.
  • Have a good understanding of performance engineering of code pipelines and near real-time systems.
  • Good understanding of Agents and MCP.
  • High-Performance Mindset: Resilience, emotional intelligence, and a focus on agile delivery.
  • Technologist DNA: A deep understanding of the difference between "coding" and "engineering."

Desired
  • Familiarity with cloud-native foundations or AI coding assistants.

Languages
  • Advanced Oral English: For seamless collaboration with global teams.
  • Advanced Spanish.

Special Notes
  • N/A

Work Arrangement
We value flexibility to support your lifestyle. This position is available as:
  • Remote

If you meet these qualifications and are pursuing new challenges, start your application on our website to join an award-winning employer. Explore all our job openings | Sequoia Career's Page: https://www.sequoia-connect.com/careers/
Requirements
  • 8-12 years of software development experience across the appropriate platform.
  • Solid IT background and experience.
  • Experience as an application developer for projects similar in scope and responsibility.
  • Strong expertise in Python, PySpark, and SQL.
  • Strong hands-on experience with AWS services such as Glue, Redshift, RDS, Lambda, Step Functions, SNS, SQS, S3, Athena, EMR, CloudWatch, and CloudTrail.
  • Experience with enterprise data lakes, data warehouses, data marts, and big data environments.
  • Strong understanding of ETL best practices, data quality, and operational support.
  • Experience with APIs, JSON, and cloud-based data integration patterns.
  • Familiarity with GitLab, Terraform, CI/CD, and AWS developer tools.
  • Experience with DynamoDB, EC2, ECS and Batch.
  • Exposure to data migration and cloud modernization programs.
  • Have a good understanding of performance engineering of code pipelines and near real time systems
  • Good understanding on Agents and MCP

Apply