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2 jobs found

14d ago

Financial Data Engineer

Equifax St. Louis, MO

St. Louis, MO

14d ago

Full time Python Apache Airflow Artificial Intelligence

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Job Description

Equifax seeks a Financial Data Engineer in St. Louis, MO onsite to design and scale data frameworks for Equifax Workforce Solutions Finance, moving from prototype to production.

Responsibilities

Assist in the design and implementation of the Equifax Workforce Solutions Finance data models in GCP
Participate in the development of strategies, standards, and best practices in the areas of data access and integration within GCP
Create recommendations for increasing data utilization based on understanding of the data delivery environment
Partner with Technology, Security, Data Governance, and IT Operations to facilitate and accelerate the movement of key Financial data assets to GCP
Stay current with rapidly developing GCP technologies landscape and share knowledge with team
Support the technology roadmap and architectural discussions, translating high-level business vision into scalable engineering deliverables within an Agile framework
Orchestrate automated data workflows and processes using Python, Airflow, Jenkins and/or Astronomer to eliminate manual intervention and increase operating efficiency
Execute rigorous software development activities, including unit testing and code deployment, while reviewing unfamiliar code within a shared codebase to ensure codebase health by completing code review, refinement and optimization
Research and evaluate emerging data sources and advanced technologies including ML, AI and RPA to enhance the existing data strategy and to optimize the output of the Finance organization
Partner with governance and compliance professionals to ensure the ethical and legal use of data assets and maintain precise lineage tracking
Collaborate with IT Centers of Excellence (COEs) to refine ETL toolsets, ensuring continuous improvements in cycle time and system performance
Author training materials and lead sessions to educate internal teams on data strategy, operating models, and evolving data capabilities
Deconstruct complex organizational problems into actionable data components to drive evidence-based solutions
Triage product or system issues and debug/track/resolve by analyzing the sources of issues, completing a root cause analysis and implementing solutions to resolve the identified issue in both the short and long-term
Orchestrate automated data workflows and processes using Python, Spark, Airflow, Jenkins and/or Astronomer to eliminate manual intervention and increase operating efficiency

Requirements

Bachelor degree in Computer Science, Information Systems, Statistics, Economics, or equivalent quantitative field with heavy emphasis on programming
5+ years of experience in building, loading, transforming, and analyzing data within and across database platforms and other data stores is required
5+ years of experience working in GCP or a similar cloud environment
5+ years of scripting/coding experience
Proficiency in one or more of the following languages / tools: Python, SQL, BigQuery, Airflow / Cloud Composer

Technologies

GCP (Google Cloud Platform)
Python
Airflow
Jenkins
Astronomer
Spark
SQL
BigQuery
Cloud Composer
Tableau
Looker
Spotfire
ML
AI
RPA

Benefits

Comprehensive compensation
Healthcare packages
401k matching
Paid time off
Organizational growth potential through online learning platform with guided career tracks

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2mo ago

Data Analytics Engineer

Request Technology Chicago, IL

Chicago, IL

2mo ago Closed

$100k - $130k/yr Full time Analytics Automation AWS

This position is no longer accepting applications (closed Jun 9, 2026).

Job Description

Based in Chicago with a hybrid work arrangement, the Data Analytics Engineer role at Request Technology centers on building and maintaining semantic layer data models and ETL and data serving solutions that power internal analytics. You will collaborate closely with the Data Platform, governance, and DevOps teams to support cloud infrastructure for analytics and to foster data literacy and self-service across the organization. The position offers a salary range of USD 100,000 to 130,000 per year and requires a bachelor’s or master’s degree in a quantitative discipline with at least three years of relevant experience.

Responsibilities

Build, test, and deploy semantic layer data models (virtual and physical) that simplify complex semi-structured data, reduce multiple definitions of similar data, create query-friendly datasets, and standardize column naming for downstream users developing quantitative analytics, dashboards, and internal risk applications.
Maintain performance and accuracy SLAs for the semantic layer and other data products through observability practices, ensuring proactive detection of system failures and timely incident response.
Learn user wants, motivations, priorities, and the why as part of eliciting business requirements with business users from various risk management departments.
Collaborate with upstream data producers to understand how their systems generate data and how changes over time can affect schema drift, helping to manage it effectively.
Work with Data Governance, Data Platform Team, and DBAs to design data access controls that meet business and internal governance needs.
Create documentation and testing to ensure data lineage is traceable and semantic layer components are easily discoverable and useful to business users.
Support the implementation of ETL and data serving solutions for large datasets generated by risk models, meeting latency and access-pattern SLAs critical to business users.
Promote self-service capabilities and data literacy for business users leveraging the semantic layer, analytics platforms (such as Tableau and Python), and CI/CD tools.
Invest in ongoing learning of data engineering best practices, cloud computing, the options trading industry, and financial risk management to improve maintainability, reliability, and utility of analytics infrastructure.
Assist risk analysts in solving analytics questions and provide ad-hoc development support as needed.

Requirements

Ability to collaborate with multiple partners including Business Users, Data and Solution Architects, Data Governance and IT teams (Data Platform Team, Systems & Infrastructure, Security, DevOps, Networking) to craft solutions that align business goals with internal processes, security, and delivery standards.
Ability to communicate technical concepts to audiences with varying levels of technical background and translate non-technical requests into technical outputs.
Comfortable supporting business analysts on high-priority projects.
High attention to detail, the ability to evaluate tradeoffs, and the capacity to think structurally about a solution.

Technologies

SQL
Python
Git
Tableau
Alteryx
dbt
AWS
Azure
Databricks
Snowflake
Linux shell
Docker
CyberArk
Hashi Vault
Jenkins
JFrog Artifactory
Harness
Apache Airflow
Dagster
Prefect

Benefits

401(k)
Dental insurance
Health insurance
Paid time off
Vision insurance

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Financial Data Engineer

Data Analytics Engineer

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