Top Cybersecurity Data & Analytics Jobs in Chicago, IL

Analyzes financial risk data and develops data transformation, reporting, dashboard, and visualization solutions for banking and capital markets clients. Supports quantitative analytics involving capital, liquidity, credit risk, and asset-liability management. The role also includes client collaboration, quality review, mentoring junior staff, process improvement, and automation of data movement and reporting.

Develop causal inference analyses and economic models using Mastercard and public datasets. Translate business questions into empirical designs, build and validate econometric and machine learning models, lead projects from data ingestion through stakeholder communication, guide causal inference best practices, prototype scalable analytics, and collaborate with clients and internal teams. Required expertise includes Python or R, SQL, quasi-experimental methods, causal machine learning, experimentation, and clear communication of technical findings.

Develop and improve IP geolocation automation algorithms using large datasets and IP network topology. Query, extract, and transform data with SQL, R, Python, MongoDB, and BigQuery; provide insights that improve data quality; and collaborate with engineers, data scientists, and stakeholders to solve complex problems and lead data science initiatives.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

Leads an eight-person analytics engineering team and sets strategy for trusted semantic models, governed data, enterprise BI, AI-powered data access, and production data applications. Oversees data modeling, evaluation datasets, telemetry, LLM routing, governance, security, and self-service capabilities. Partners cross-functionally to establish engineering standards, roadmaps, and measurable business impact while hiring, coaching, and developing team members.

Designs and manages scalable data pipelines, contracts, transformation layers, and analytics models. The role focuses on data governance, observability, lineage, configuration validation, and collaboration with product engineering teams. It requires processing large datasets, integrating cloud and on-premises industrial technologies, and implementing streaming and message-queuing solutions, ideally in cybersecurity or industrial environments.

The Senior Data Analyst gathers BI requirements, analyzes enterprise and offline datasets, writes and optimizes SQL queries, and builds dashboards using Power BI, Tableau, and Domo. The role translates business needs into technical specifications, designs dashboard wireframes, validates data, documents processes, and collaborates with engineering, product, and business teams. Knowledge of the US trust business is essential for interpreting data within financial and regulatory contexts.

Establishes and maintains data governance for a government program, including ownership, glossaries, dictionaries, metadata, lineage, approved-use records, retention rules, identifiers, and authoritative sources. Coordinates data decisions among government stakeholders, engineers, analysts, AI/ML, cybersecurity, and reporting teams. Tracks governance issues, supports data quality and access controls, maintains documentation, assesses data changes, and reports governance status and priorities.

Supports data modernization by assessing legacy structures, designing modern data architectures, developing ETL/ELT pipelines, integrating APIs and cloud services, and ensuring data quality, security, governance, and lineage. Analyzes data for migration and reconciliation, creates dashboards and reports, and collaborates with technical and business teams. Applies AI tools to improve software development, DevSecOps, testing, security, deployment, and automation processes.