Chicago, IL

Data Jobs in Chicago, IL

25 jobs found

Include remote jobs

Today

Staff Data Engineer

Okta Chicago, IL

Chicago, IL

Today

$174k - $239k/yr Full time Senior Apache Airflow Cloud

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Job Description

Okta is seeking a Staff Data Engineer to join the Auth0 Data Engineering Team in Chicago, IL on a hybrid schedule. This role leads the architectural evolution of the core data platform, starting with FEDRamp compliance, and drives scalable, secure designs across multiple teams. The position offers a salary range of USD 174,000 to 238,700 per year, with benefits that emphasize well-being, social impact, and talent development within a collaborative culture.

Benefits

Supporting Your Well-Being
Driving Social Impact
Developing Talent and Fostering Connection + Community

The Auth0 Data Engineering Team

Pipeline team — sits near the Platform team and data producers, responsible for efficient data ingestion and rapid access to unmodeled data
Warehouse team — aligns with business teams and data consumers, modeling data in the data warehouse to abstract complexity and simplify analysis, reporting, and decision-making
Interface team — builds and manages connections between the data platform and external systems to ensure consistent and reliable data access

Responsibilities

Spearhead the evolution of the core data platform, shaping the long-term architectural roadmap
Set the technical direction for large-scale platform improvements, balancing visionary goals with incremental delivery
Collaborate with engineers and leaders across data, platform, and security to deliver a cohesive and secure architecture
Create architectural artifacts such as design documents, threat models, and technical specifications to drive clarity and alignment on complex initiatives
Serve as a technical mentor and multiplier for the Data Engineering team, elevating performance through guidance, code reviews, and sharing best practices
Build foundational components of the new architecture, validating designs with hands-on coding and serving as a model for other engineers

Requirements

A passion for building secure, reliable, and scalable data systems
8+ years of software development experience, with at least 5+ years in a data engineering or data platform role
Proven experience designing and delivering large-scale, cross-functional technical projects
Extensive experience architecting and deploying data infrastructure in a cloud environment, with AWS strongly preferred
Deep architectural knowledge of the modern data stack, including data warehouses (Snowflake), transformation tools (dbt), and orchestrators (Airflow)
Strong proficiency in Python and SQL, with a high bar for code quality and system design
Excellent communication skills, with the ability to create clarity and drive decisions on complex technical topics
Ability to deal with ambiguity, define a path forward, and lead others to successful outcomes

Technologies

Technologies: Snowflake, dbt, Airflow, Python, SQL, AWS, Terraform, Kubernetes

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1d ago

Data Scientist II, Anti Money Laundering Transaction Monitoring

BMO Financial Group Chicago, IL

Chicago, IL

1d ago

$69k - $128k/yr Full time Ai Ml AML Aml Ctr

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Job Description

BMO Financial Group is seeking a Data Scientist II to advance its AML transaction monitoring program from the Chicago area. The role centers on the design, development, and deployment of machine learning and analytical models for detecting money laundering activity and supporting the model lifecycle, with collaboration across AML Compliance, investigations, governance, and technology teams.

Responsibilities

Design, build, test, and deploy machine learning and analytical models used for AML transaction monitoring and suspicious activity detection
Analyze large volumes of customer, transactional, payment, and alert data to identify emerging money laundering risks, typologies, and anomalous behavior patterns
Develop and evaluate supervised and unsupervised machine learning approaches to improve detection effectiveness and investigator outcomes
Conduct feature engineering and exploratory data analysis to identify behavioral indicators associated with financial crime risk
Perform quantitative assessments of model performance, including detection effectiveness, productivity, false positive reduction, and risk coverage
Support model tuning, optimization, and revalidation activities to ensure models continue to perform as intended
Assess data quality and data lineage and partner with data management teams to resolve issues affecting model performance and reliability
Prepare clear and comprehensive model development, testing, and governance documentation to support model validation, audit, and regulatory reviews
Translate analytical findings into actionable recommendations for AML Compliance, FIU, and senior management
Collaborate with business stakeholders, investigators, model governance, and technology partners to prioritize enhancements and implement solutions
Research emerging financial crime typologies, machine learning techniques, and industry best practices to continuously improve monitoring capabilities
Contribute to strategic initiatives involving AI, machine learning, graph analytics, network analysis, and other advanced analytical approaches applicable to financial crime detection
Ensure all analysis and model development activities comply with regulatory requirements, internal policies, and model risk management standards
Take measured risks while protecting the bank by applying the Risk Management Framework and exercising sound risk-based judgment

Requirements

Typically between 4 - 6 years of relevant experience and a post-secondary degree in a related field or an equivalent combination of education and experience
Experience with Python and SQL for large-scale data analysis, feature engineering, and model development
Strong understanding of statistical analysis, machine learning algorithms, and model performance measurement
Experience working with large structured and semi-structured datasets
Experience using version control tools and code repositories (e.g., Git, GitHub, Azure DevOps) to support collaborative development and reproducible analytical workflows
Familiarity with generative AI tools and AI-assisted development practices (e.g., Microsoft 365 Copilot, GitHub Copilot, LLM-based coding assistants) to improve productivity, documentation, and analytical workflows
Ability to communicate technical concepts effectively to both technical and non-technical audiences

Technologies

Python
SQL
Git
GitHub
Azure DevOps
Dataiku
Databricks
SAS
Spark
Hadoop
Microsoft 365 Copilot
GitHub Copilot
LLM-based coding assistants
Cloud-based analytics platforms

Benefits

Performance-based incentives
Discretionary bonuses
Health insurance
Tuition reimbursement
Accident and life insurance
Retirement savings plans

Application deadline

08/04/2026

Location

Chicago, IL (onsite)

Address

320 S Canal Street

Salary

USD 69,000 - 127,800 per year — Salaried

Job Family Group

Data Analytics & Reporting

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2d ago

Data Analyst

Bridgeline Solutions Chicago, IL

Chicago, IL

2d ago

$40 - $55/hr Contract Analyst Analytical Skills Analytics

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Job Description

Bridgeline Solutions is seeking an HR Data Analyst to drive workforce reporting and analytics for its Chicago, IL team. The role operates on a hybrid schedule and begins with a 3-month contract, with a strong likelihood of extension.

Responsibilities

Analyze workforce data, both quantitative and qualitative, and produce clear, accurate reports
Create and maintain recurring reports and dashboards for internal stakeholders
Compute representation, retention, attrition, and other key workforce metrics
Provide validated, reliable information in response to internal and client data requests
Support demographic self identification efforts and workforce data initiatives
Identify and report staffing, workforce, and assignment trends over time
Prepare submissions for external surveys, certifications, rankings, and benchmarking programs
Collaborate with stakeholders across the organization to deliver meaningful insights and recommendations

Requirements

At least 3 years of experience in a data analysis, reporting, or workforce analytics role
Experience in a law firm environment
Advanced Microsoft Excel skills, including pivot tables, complex formulas, trend analysis, and rate calculations
Experience with Tableau, Power BI, or a similar business intelligence platform
Strong data validation, reconciliation, and comparative analysis abilities
Experience working with databases and structured data sources
Ability to communicate findings and methodologies to non technical audiences
Proficiency with Microsoft Office applications
Bachelor's degree
Experience with HRIS, CRM, or other workforce reporting systems
SQL or other data querying and scripting experience
Experience in a law office setting for at least 1 year is required

Technologies

Tableau
Power BI
Microsoft Excel
SQL
HRIS
CRM

Details

Location: Chicago, IL (Loop)
Schedule: Hybrid, three days onsite per week
Hours: Monday through Friday
Duration: Initial 3-month contract with strong potential for extension

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3d ago

Data Engineer

SN Cloud Solutions Chicago, IL

Chicago, IL

3d ago

$0k+/yr Full time Python Apache Airflow Azure Data Factory

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Job Description

SN Cloud Solutions is seeking a Data Engineer to join its Chicago team in a hybrid arrangement. The role focuses on designing, building, and maintaining scalable data pipelines and data platforms that underpin enterprise analytics and BI, leveraging Python, PySpark, SQL, and cloud ETL/ELT tools. You will partner with data scientists, analysts, and business stakeholders to turn raw data into reliable insights.

Responsibilities

Architect and implement ETL/ELT pipelines for large-scale data processing.
Create scalable data solutions using Python, PySpark, and Apache Spark.
Build and sustain data ingestion frameworks for both structured and unstructured data.
Develop and tune SQL queries, stored procedures, and database objects.
Ingest and consolidate data from diverse sources, including APIs, databases, and cloud storage.
Establish data quality, validation, and monitoring mechanisms.
Deploy and operate on cloud platforms such as AWS, Azure, or GCP.
Create and manage data lakes and data warehouses.
Partner with data scientists, analysts, and business stakeholders.
Orchestrate workflows with tools such as Apache Airflow.
Diagnose production issues and optimize pipeline performance.
Follow Agile/Scrum practices and CI/CD processes.

Requirements

Experience as a Data Engineer.
Strong experience with Python and PySpark.
Hands-on experience with Apache Spark.
Strong SQL programming skills.
Experience with ETL/ELT development.
Experience with cloud platforms (AWS, Azure, or GCP).
Knowledge of data warehousing concepts.
Experience with Apache Airflow or similar orchestration tools.
Experience with Git, Jenkins, or Azure DevOps.
Strong understanding of data modeling and database design.
Experience with relational and NoSQL databases.
Excellent problem-solving and communication skills.

Experience

Azure Data Factory: 7 years (Required)
SQL: 3 years (Required)
Data warehouse: 4 years (Required)

Technologies

Python
PySpark
Apache Spark
SQL
AWS
Azure
GCP
Apache Airflow
Databricks
Kafka
Delta Lake
Snowflake
Redshift
BigQuery
Synapse
Azure Data Factory
Git
Jenkins
Azure DevOps

Benefits

Flexible schedule

Job Type

Full-time

Pay

From USD 50.00 per year

Work Location

Hybrid remote in Chicago, IL 60618

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3d ago

Applied AI Engineer I

Deloitte Chicago, IL

Chicago, IL

3d ago

$84k - $155k/yr Full time Agentic Ai AI Artificial Intelligence

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Job Description

Applied AI Engineer I in Chicago, IL (onsite) at Deloitte, holding a Bachelor's degree, with a salary range of USD 84,300 - 155,400 per year, focuses on integrating GenAI and agentic capabilities into full-stack products.

Responsibilities

Outcome driven accountability: own and promote a culture focused on customer and business outcomes, including managing the cost of achieving them. design lean, high-quality solutions that solve complex problems and take responsibility for inference, tokens, and cloud costs.
Technical leadership and advocacy: act as the technical champion for products, ensuring code integrity, feasibility, and alignment with business and customer goals. contribute to requirement analysis, component design, development, testing, integrations, and support.
Engineering craftsmanship: uphold code-design integrity and adherence to architecture and tech stack; ensure quality, data integrity, and ongoing maintenance and operations. stay hands-on, proactive, and continuously learn new languages and frameworks. create technical specifications and deliver scalable, maintainable code that meets quality KPIs. collaborate effectively with diverse teams.
Customer centric engineering: develop lean engineering solutions through rapid, low-cost experimentation to address customer needs. engage with customers and product teams before, during, and after delivery to ensure the right solution is delivered at the right time.
Incremental and iterative delivery: prioritize action and evidence over extensive planning; navigate complexity with a forward-leaning approach to deliver lean, maintainable solutions.
Cross functional collaboration and integration: work with empowered, cross-functional teams including product management, experience, and delivery. integrate diverse perspectives to balance feasibility, viability, usability, and value. foster a collaborative environment that enhances team synergy and innovation.
Advanced technical proficiency: build expertise in modern software engineering practices and AI driven agentic SSDLC to enable daily product deployments with full automation from discovery to production to operations, including quality checks throughout the SSDLC lifecycle. model these techniques to optimize solutioning and delivery.
Domain expertise: rapidly acquire domain knowledge relevant to the business or product. translate business and user needs, architectures, and UI/UX designs into technical specifications and code. be a flexible, quality-focused team member committed to quality and reducing tech debt.
Effective communication and influence: demonstrate exceptional communication skills to articulate complex technical concepts clearly. influence teammates and product teams with evidence-based trade-offs and coherent narratives that align technical solutions with business objectives.
Engagement and collaborative co-creation: engage with product engineering teams at all levels, including customers as needed. build and maintain constructive relationships that foster co-creation and momentum toward product goals. align diverse perspectives and drive consensus to deliver feasible solutions.

Requirements

Ability to work independently and collaborate as part of a team
Effective written and verbal communication skills
Meticulous attention to detail and quality of work product
Ability to build and sustain professional relationships
Ability to lead projects or workstreams
Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment
Strong interpersonal skills and professional demeanor
Ability to meet deadlines
Ability to provide clear guidance to others

Technologies

Angular, React, NodeJS, Python, C#, .NET, Java
SQL/NoSQL, PyTorch, TensorFlow
LangChain, LangGraph, MLflow, LangFuse, LangSmith
OpenAI, Anthropic, Vertex AI, Azure OpenAI, AWS Bedrock
Azure, AWS, GCP
RAG pipelines, vector databases, prompt engineering, multi-agent orchestration tools

Benefits

Discretionary annual incentive program

The Team

US Deloitte Technology Product Engineering has modernized software and product delivery to create a scalable, cost-effective model focused on value and outcomes. As Deloitte's primary internal development team, Product Engineering delivers innovative digital solutions to businesses, service lines, and internal operations, backed by proven bottom-line results and outcomes that power Deloitte's success.

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4d ago

Senior Lead Data Engineer

Information Technology Senior Management Forum Chicago, IL

Chicago, IL

4d ago

$209k - $239k/yr Full time Senior Big Data Cloud Platform

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Job Description

Join Information Technology Senior Management Forum as a Senior Lead Data Engineer, guiding data engineering initiatives, architecting cloud-based data platforms, and mentoring a team of engineers. This role emphasizes collaboration across Agile teams and cross-functional partners to solve complex business problems with scalable data solutions.

Location

Chicago, IL (onsite)

Salary

USD 209,000 - 238,500 per yearly

Responsibilities

Collaborate with and across Agile teams to design, develop, test, implement, and support technical solutions across full-stack development tools and technologies
Lead a team of developers with deep experience in machine learning, distributed microservices, and full-stack systems
Use programming languages such as Java, Scala, and Python, along with Open Source RDBMS and NoSQL databases and cloud-based data warehousing services like Redshift and Snowflake
Share a passion for staying current with tech trends, experimenting with new technologies, participating in internal and external technology communities, and mentoring other engineers
Collaborate with digital product managers to deliver robust cloud-based solutions that drive powerful experiences to help millions of Americans achieve financial empowerment
Perform unit tests and conduct code reviews to ensure solutions are rigorously designed, well-coded, and optimized for performance

Requirements

Bachelor’s Degree
Minimum 1 year of overall experience
At least 6 years of experience in application development (Internship experience does not apply)
At least 2 years of experience in big data technologies
At least 1 year of experience with cloud computing (AWS, Microsoft Azure, Google Cloud)

Technologies

Java
Scala
Python
Open Source RDBMS
NoSQL databases
Redshift
Snowflake
AWS
Microsoft Azure
Google Cloud
MapReduce
Hadoop
Hive
EMR
Kafka
Spark
Gurobi
MySQL
Mongo
Cassandra
UNIX/Linux
shell scripting
interactive AI tooling

Type

Full-time

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5d ago

Lead Data Scientist

Hyatt Corporate Office Chicago, IL 🇺🇸 Remote

Chicago, IL

5d ago

$160k - $170k/yr Full time Ai Ml Artificial Intelligence Data

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Job Description

Lead Data Scientist, individual contributor on Hyatt's AIML team focused on Search, Personalization, and Agents.

Responsibilities

Own the design, development, evaluation, and optimization of AI and ML solutions that support Hyatt’s guest, colleague, and operational experiences.
Provide technical leadership, mentor peers, influence architecture and product direction, and raise the overall technical bar for applied AI at Hyatt.
Serve as a hands-on technical lead for high impact AI and machine learning initiatives.
Lead solution design, modeling decisions, experimentation strategy, and technical tradeoff discussions.
Partner with ML engineering and data engineering teams to deploy scalable real-time inference pipelines and batch processing workflows.
Influence technical roadmaps and help sequence data science initiatives based on business value, feasibility, risk, and team capacity.
Mentor data scientists and ML practitioners through design reviews, code reviews, modeling best practices, and knowledge sharing.
Collaborate with ML engineering to productionize models and Gen AI services using AWS-native tools and modern MLOps practices.
Contribute to scalable ML system design, including data pipelines, feature workflows, model serving, observability, monitoring, and lifecycle management.
Apply strong software engineering practices, including version control, CI/CD, testing, reproducibility, containerization, and documentation.
Support deployment patterns for both batch and low-latency inference use cases.
Partner with security, governance, architecture, and legal/privacy stakeholders to ensure AI systems are reliable, secure, compliant, and responsibly deployed.
Work closely with product owners, data scientists, ML engineers, data engineers, architects, and business stakeholders to deliver end-to-end algorithmic products.
Communicate model behavior, limitations, assumptions, risks, and business impact clearly to technical and non-technical audiences.
Define measurable success criteria and help evaluate whether AI solutions are delivering intended outcomes.
Champion responsible AI, inclusive design, and practical experimentation across projects.

Requirements

Master’s degree in computer science, software engineering, or a related field; PhD preferred.
Six or more years of experience in machine learning roles with a focus on NLP/NLU, reinforcement learning, or large language model applications.
Three or more years of people management experience in a tech leadership role.
Experience fine-tuning and deploying LLMs or other Generative AI solutions to production.
Proficiency with AWS cloud services such as SageMaker, ECS/EKS, Step Functions, Lambda, and Glue.
Strong Python programming skills, with familiarity in SQL, PySpark, and containerization using Docker.
Proven experience designing scalable data pipelines and ML systems for both real-time and batch inference.
Deep understanding of responsible AI practices, CI/CD pipelines, Agile development practices, and model lifecycle management.
Excellent interpersonal and communication skills, with a strong bias for action and collaboration.
Familiarity with ML observability and governance tools.

Technologies

Python
SQL
PySpark
Docker
SageMaker
ECS
EKS
Step Functions
Lambda
Glue

Benefits

Annual allotment of free hotel stays at Hyatt hotels globally
Flexible work schedule
Headspace subscription
Discount at on-site fitness center
Paid time off following the birth or adoption of a child
Financial assistance for adoption
Paid Time Off
Medical, Dental, Vision coverage
401K with company match

Qualifications

Master’s degree in computer science, software engineering, or a related field; PhD preferred.
Six or more years of experience in machine learning roles focused on NLP/NLU, reinforcement learning, or LLM applications, including at least three years in a tech leadership role.
Experience fine-tuning and deploying LLMs or other Generative AI solutions to production.
Expertise in AWS cloud services (SageMaker, ECS/EKS, Step Functions, Lambda, Glue).
Strong Python skills with SQL, PySpark, and Docker experience.
Proven ability to design scalable data pipelines and ML systems for real-time and batch inference.
Deep understanding of responsible AI, CI/CD, Agile methods, and model lifecycle management.
Excellent communication and collaboration skills.
Familiarity with ML observability and governance tools.