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Internship Financial Data Engineer Jobs in Georgia

M3

Senior Data Engineer

Lawrenceville, GA · On-site

$97K - $132K/yr

The Lead Data Engineer plays a foundational role in designing and implementing this platform on ... Lead scalable data modeling efforts across financial, customer, and product domains using domain ...

M3

Senior Data Engineer

Lawrenceville, GA · On-site

$97K - $132K/yr

The Lead Data Engineer plays a foundational role in designing and implementing this platform on ... Lead scalable data modeling efforts across financial, customer, and product domains using domain ...

Voya Financial

Senior Data Engineer

Atlanta, GA · On-site +1

$101K - $138K/yr

Together we fight for everyone's opportunity for a better financial future. We will do this ... Implement enterprise solutions, in a cloud environment, using a vast range of data engineering and ...

Mwe

Pricing Data Engineer

Atlanta, GA

$110K - $169K/yr

That's why our benefits program supports your physical, emotional, mental, and financial health ... The Pricing Data Engineer builds and maintains the data infrastructure and tools that enable ...

R2 Technologies Corporation

Big Data Engineer

Alpharetta, GA · On-site

$54.50 - $72/hr

Big Data Engineer Joining Location: Remote due to Covid but eventually (Alpharetta, GA) # of ... financial company or other regulated entity managing sensitive and confidential information • ...

Stefanini Group

Senior Data Engineer

Atlanta, GA · Remote

$102K - $138K/yr

Stefanini is looking for a Senior Data Engineer -Remote For quick apply, please contact Vaibhav ... markets, including financial services, manufacturing, telecommunications, chemical services ...

Stefanini

Senior Data Engineer

Atlanta, GA · On-site

$101K - $138K/yr

Stefanini is looking for a Senior Data Engineer-Remote For quick apply, please contact Vaibhav ... markets, including financial services, manufacturing, telecommunications, chemical services ...

ClarityPay

Finance Data Analyst

Atlanta, GA · On-site

... to extract and analyze data that drives financial reporting and business decisions ... engineering to understand data models, ensure data quality, and flag anomalies • Help build and ...

Stefanini Group

Senior Data Engineer

Atlanta, GA · Remote

$102K - $138K/yr

Stefanini is looking for a Senior Data Engineer -Remote For quick apply, please contact Vaibhav ... markets, including financial services, manufacturing, telecommunications, chemical services ...

Stefanini Group

Senior Data Engineer

Atlanta, GA · Remote

$102K - $138K/yr

Stefanini is looking for a Senior Data Engineer -Remote For quick apply, please contact Vaibhav ... markets, including financial services, manufacturing, telecommunications, chemical services ...

DPR Construction

Data Engineering Lead- Finance

Atlanta, GA

$110K - $132K/yr

We are looking for a talented Data Engineer to join our team and contribute to developing robust data solutions that support our business goals. This role is ideal for someone who enjoys combining ...

HUB

Master Data Management Engineer

Atlanta, GA · On-site

$110K - $132K/yr

... interns in close coordination with team Managers and Directors QUALIFICATIONS (REQUIRED AND ... GCP Professional Data Engineer certification is a plus * Knowledge of best practices in enterprise ...

Frazier & Deeter

Senior Data Engineer

Atlanta, GA · Hybrid

$101K - $138K/yr

Design, build, and maintain trusted data foundations across financial, practice-management, tax ... Collaborate with AI Enablement Engineers to make trusted data available for automations, agents ...

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All JobsFinancial Data Engineer JobsInternship Financial Data Engineer JobsInternship Financial Data Engineer Jobs in Georgia

Internship Financial Data Engineer information

What are the key skills and qualifications needed to thrive as an internship financial data engineer?

To thrive as an Internship Financial Data Engineer, you need a solid grasp of statistics, programming (especially Python or R), and foundational knowledge of finance or economics, typically supported by relevant coursework or a related degree. Familiarity with data visualization tools (like Tableau), SQL databases, and cloud platforms such as AWS or Azure is often expected. Strong analytical thinking, attention to detail, and effective communication skills help you interpret complex data and collaborate with teams. These abilities are crucial for transforming raw financial data into actionable insights and supporting data-driven decision-making in financial organizations.

What is the difference between Internship Financial Data Engineer vs Financial Data Analyst?

AspectInternship Financial Data EngineerFinancial Data Analyst
Required CredentialsCurrently pursuing or recently completed a degree in finance, data science, or related fields; some programming knowledgeBachelor's degree in finance, economics, or related fields; proficiency in data analysis tools
Work EnvironmentInternship setting, often in finance or tech companies, focusing on data pipeline developmentOffice environment, analyzing financial data, creating reports, and supporting decision-making
Employer & Industry UsageUsed by financial institutions, tech firms, and investment companies for data engineering tasksCommon in banks, investment firms, and corporate finance departments for data analysis

The main difference is that an Internship Financial Data Engineer focuses on building and maintaining data infrastructure during an internship, often involving programming and data pipeline work. In contrast, a Financial Data Analyst primarily interprets and reports on financial data to support business decisions. Both roles require a strong understanding of finance and data tools but differ in their core responsibilities and work environment.

What does an internship financial data engineer do?

An Internship Financial Data Engineer assists in building and maintaining data systems that support financial analysis and decision-making. They work with large datasets, help develop data pipelines, and ensure data quality and integrity for financial applications. Interns may use programming languages like Python or SQL, and tools such as databases and cloud platforms, to process and analyze financial data. Their work supports the broader data engineering team and helps improve the efficiency of financial data management within the organization.

What are the most commonly searched types of Financial Data Engineer jobs in Georgia?

The most popular types of Financial Data Engineer jobs in Georgia are:

What are popular job titles related to Internship Financial Data Engineer jobs in Georgia?

For Internship Financial Data Engineer jobs in Georgia, the most frequently searched job titles are:

What job categories do people searching Internship Financial Data Engineer jobs in Georgia look for?

The top searched job categories for Internship Financial Data Engineer jobs in Georgia are:

What cities in Georgia are hiring for Internship Financial Data Engineer jobs?

Cities in Georgia with the most Internship Financial Data Engineer job openings:

Internship Financial Data Engineer jobs near you

Senior Data Engineer

Intercontinental Exchange Holdings, Inc.

Atlanta, GA • On-site

$101K - $138K/yr

Full-time

Re-posted 3 days ago

Job description

Overview
Job Purpose
ICE Data Services (an Intercontinental Exchange company) is seeking a Senior Data Engineer to join its Data Impact & Innovation team. This team supports a variety of reference data, index, climate finance, and alternative data products. The role contributes to the data platforms and pipelines that help the financial sector understand and respond to carbon transition risk, physical risk, and related challenges.
Our team maintains a global-scale geospatial data platform in Google BigQuery, holding many terabytes of data across carbon transition risk, physical climate risk, and social/demographic features - feeding analytical products for fixed income and real estate financial instruments, supporting the ambitious product roadmap for ICE Climate and other data products. Our engineering stack includes:
  • Orchestration: Airflow, moving toward composable task abstractions over a shared pipeline framework
  • Transformation: dbt, and other data lineage and DQA tools, primarily using Google BigQuery
  • Geospatial processing: Python (GeoPandas, Shapely, GeoAlchemy2 against PostGIS) for vector operations, and R
  • Execution and compute environments: Hybrid across Google Cloud Platform and on-premise RHEL Linux infrastructure
  • Ingestion: Third-party vendor feeds via API, SFTP, cloud storage, and database replication

Typical engineering challenges include working with data science and climate science teams in operationalizing trained models and data pipelines, absorbing upstream vendor corrections and historical restatements without corrupting downstream artifacts, scaling raster x vector joins at terabyte scale, evolving schemas and spatial-indexing strategies as data sources broaden, and balancing long-running batch workflows against emerging sub-daily refresh cadences.
Responsibilities
  • Take significant components of the data platform from "works" to "mature" - tightening reliability, observability, cost/performance characteristics, and operational discipline across our ingestion, transformation, and serving layers.
  • Establish and foster adoption of technical standards for the team's work - including Airflow DAG structure, dbt model layout, BigQuery schema and partitioning conventions, pipeline testing practices, and deployment workflows.
  • Lead technical design discussions, mentor other data engineers through code review, pairing, and design-doc review, and grow them along their career path.
  • Act as a technical point of contact for cross-functional initiatives - partnering with data science, climate science, product, and infrastructure colleagues to drive forward decisions and make tradeoffs explicit.
  • Deliver day-to-day work across the stack above - authoring Airflow DAGs and dbt models, contributing geospatial processing capabilities, and shipping cleanly partitioned, audit-friendly outputs from ingestion through serving.
  • Support data science and climate science teams by helping design the tooling, training, and validation environments, and by deploying their trained models into production.
  • Effectively leverage AI and LLM-based developer tooling to accelerate development workflows and improve code quality.
  • Identify opportunities to improve and optimize data pipelines - for speed, cost, robustness, integrity, and operational simplicity.
  • Work with business analysts, product management, and adjacent engineering teams to understand and refine new data requirements.

Knowledge and Experience
  • 5+ years of professional experience as a data engineer, with a track record of architecting, shipping, and operating production data pipelines end-to-end.
  • Experience mentoring and developing other data engineers - through code review, pairing, design discussions, and career coaching.
  • Ability to establish and foster adoption of technical standards.
  • A habit of actively monitoring, evaluating, and prototyping emerging big-data, geospatial, and machine-learning technologies and platforms - staying conversant in advances across cloud data engines, geospatial libraries and standards, and ML/MLOps frameworks - and bringing the most promising into the team's design discussions, evaluations, and adoption decisions.
  • Strong system-design judgment across the tradeoff space of performance, cost, maintainability, and auditability
  • Comfort scoping, decomposing, and delegating work for other engineers.
  • Strong written and verbal communication - able to translate technical tradeoffs for senior business, product, and client stakeholders.
  • Deep fluency in modern, typed Python as a primary working language, including comfort with type-driven design (e.g. Pydantic v2).
  • Strong SQL background, including experience partitioning, clustering, and performance-tuning queries on modern cloud warehouses - Google BigQuery experience strongly preferred.
  • Production experience with dbt for managing warehouse transformations, and with Airflow (or a comparable orchestrator) for workflow orchestration.
  • Solid grounding in geospatial data engineering - Python tooling (GeoPandas, Shapely), spatial databases (PostGIS), raster processing, or adjacent skills.
  • A systems-thinking orientation: anticipates cascading effects of upstream data changes, schema evolution, and vendor corrections; designs pipelines with observability, auditability, and graceful failure in mind.
  • Comfort owning production incidents and debugging distributed systems.
  • Experience working cooperatively with systems, network, and infrastructure engineering and operations teams to ensure proper monitoring, alerting, and incident response workflows.
  • Demonstrated ability to integrate AI/LLM coding assistants productively - treating them as a force multiplier rather than a substitute for judgment.
  • Curiosity about the financial and climate/geospatial domains and contexts the team operates in.

Preferred Knowledge and Experience
  • Well-versed in and opinionated about the modern Python ecosystem.
  • Exposure to columnar and lakehouse technologies (Parquet, ClickHouse, DuckDB).
  • Working understanding of data lineage, data quality validation, and metadata/cataloging frameworks.
  • Prior experience in a hybrid cloud + on-premise environment, and with full software development lifecycle (SDLC) best practices and processes.
  • Prior exposure to ML deployment workflows - supporting data science teams with training tooling and/or model-serving infrastructure.
  • Familiarity with R, particularly geospatial packages.

#LI-HR1 #LI-ONSITE
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Intercontinental Exchange, Inc. is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to legally protected characteristics.

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