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Remote Python Sql Tableau Jobs in Chicago, IL (NOW HIRING)

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Monetization Strategy Specialist

Chicago, IL · On-site +1

$105K - $163K/yr

Employee divides their time between in-office and remote work. Access to an office location is ... Proficiency in data tools (e.g., Excel, SQL, Tableau) with the ability to interpret complex ...

Meta

Energy Analyst, New Transactions

Chicago, IL · On-site +1

$132K/yr

Experience coding in Python, SQL, or similar languages to automate data processing and build ... Proficiency with data visualization tools such as Tableau or equivalent platforms to communicate ...

VIVA USA INC

Application Development Senior Advisor

Chicago, IL · On-site +1

$99K - $136K/yr

Analyze and enhance SQL queries to improve performance, ensuring that queries/stored procedures are ... Remote VIVA is an equal opportunity employer. All qualified applicants have an equal opportunity ...

Motorola

Data Strategist - M&A (US Remote)

Chicago, IL · Remote

$130K - $150K/yr

... AWS, SQL, Tableau, etc) * Financial Modeling: Demonstrated experience building complex financial ... Candidates in Chicago working on a hybrid schedule is preferred. #LI-MP2 #LI-REMOTE Basic ...

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How much do remote python sql tableau jobs pay per hour?

As of Jul 7, 2026, the average hourly pay for remote python sql tableau in Chicago, IL is $60.39, according to ZipRecruiter salary data. Most workers in this role earn between $49.76 and $68.61 per hour, depending on experience, location, and employer.

What is the difference between Remote Python Sql Tableau vs Data Analyst?

AspectRemote Python Sql TableauData Analyst
Required SkillsPython, SQL, Tableau, data visualizationExcel, SQL, data visualization, basic statistics
Work EnvironmentRemote, collaborative teams, project-basedRemote or on-site, business-focused
Industry UsageTech, finance, healthcare, marketingFinance, retail, healthcare, consulting

Remote Python Sql Tableau roles focus on programming, data manipulation, and visualization, often requiring coding skills. Data Analysts analyze data to generate insights, typically using Excel and SQL. While both roles involve data visualization, Python and Tableau are more prominent in Remote Python Sql Tableau positions, whereas Data Analysts may rely more on Excel and basic tools. Understanding these differences helps job seekers target the right roles based on their skills and career goals.

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Remote Python Sql Tableau jobs near you
Manager, Data Engineering- Data Visualization (Remote)

Manager, Data Engineering- Data Visualization (Remote)

Inspira Financial

Oak Brook, IL • On-site, Remote

Full-time

Posted 3 days ago

Inspira Financial rating

7.2

Company rating: 7.2 out of 10

Based on 20 frontline employees who took The Breakroom Quiz

Job description

The Data Engineering Manager - Enterprise Data Visualization will report to the Sr. Director, Data Engineering in the Technology Department. This role will engage with Business Leaders, Analysts, Data Stewards, Application Architects, and third-party providers. This role will lead an agile team of Data Engineers that work to enhance data delivery, quality, accessibility, and analysis. This role will improve functionality, streamline data processes, provide direct support to the business and operational teams, and strengthen targeted business strategies. The role works closely with the Business, Operations and Technology groups to help design and lead the development and maintenance of the Enterprise Reporting Platform.
If you are ready to advance your career and contribute to a rapidly growing company dedicated to delivering innovative products and ensuring an exceptional client experience, we eagerly await your application!
Duties & Responsibilities:
  • Lead an agile team of data engineers with varying levels of experience in the delivery of enterprise data initiatives.
  • Oversee the creation of data visualizations including enterprise dashboards, analytical, and operational reporting.
  • Partner with business leadership to implement 3-5-year plan for area of responsibility.
  • Manage multiple concurrent projects.
  • Define and establish benchmarks, metrics, and quality measures.
  • Support disaster recovery and contingency planning.
  • Ensure solutions meet non-functional requirements, including security, performance, maintainability, scalability, usability, and reliability.
  • Effectively manage relevant 3rd party vendor relationships.
  • Support the stability and resiliency of Data Visualization production processes, as well as instituting a robust support model addressing process and application failures.
  • Understand the business and technology.
  • Drive process alignment with business partners.
  • Identify project team requirements and capital requirements.
  • Evaluate and integrate productivity tools, development tools, testing tools, databases, and applications into this architecture.
  • Work with the leaders of Technology Infrastructure and Software Engineering to ensure effective operational tools and procedures are in place to support the application architecture.
  • Research and strategize emerging technologies relevant to business needs.
  • Develop and document an overall enterprise reporting delivery architecture which is fit for business purposes and cost effective.

Supervisory Responsibilities:
  • Recruits, interviews, hires, and trains new staff.
  • Oversees the daily workflow of the department.
  • Provides constructive and timely performance evaluations.
  • Participate in budget planning and monitoring.

Education & Experience:
  • 10+ years of experience in Data Engineering, Data Visualization, or Software Product Development
  • Bachelor's degree preferred in Computer Science, Computer Engineering, Software Engineering, Electrical/Electronic Engineering, Mathematics, Statistics, Data Science, or similar/related Engineering/Science based disciplines
  • 1-3 years of leadership experience managing direct reports
  • Tableau Certifications are preferred
  • Microsoft Certified Azure Data Fundamentals preferred
  • Snowflake SnowPro Certification preferred

Skills & Abilities:
  • Strong understanding of Programming Skills. While not expected to perform day-to-day code development, the Data Engineering Manager is expected to be knowledgeable and practiced in programming languages such as SQL/T-SQL, Python
  • Data / Database Skills: Competence with relational and NoSQL databases (e.g., SQL Server, MongoDB) including proficiency with Data Definition Languages, Data Mark-Up Languages
  • Participate in design and implementation of OLAP databases to serve internal and external consumer use cases
  • Design and implement hybrid data cloud services, leveraging public could providers (i.e., Azure) and specialty providers (i.e., Snowflake)
  • Understand and implement Generative AI solutions within the context of developer assistance and data visualization product delivery
  • Support the development of enterprise data visualization strategy ensuring rapid delivery while taking responsibility for applying standards, principles, theories, and concepts
  • Support enterprise data governance initiatives
  • Strong experience in working with and optimizing enterprise reporting
  • Exceptional analytical skills and strong attention to detail
  • Ability to prioritize, plan and take initiative
  • Highly self-motivated and directed
  • Experience in a high availability environment preferred
  • Knowledge of ITIL/ITSM Foundational practices and framework preferred
  • Strong Vendor management skills preferred
  • Strong understanding of Salesforce Financial Services Cloud data object model preferred
  • Data Engineering Tools/Platforms
    • Platform/Framework (Snowflake, Azure MSSQL)
    • Visualization (Tableau, PowerBI, SSRS)
    • Governance (Data.World, Atlan, Alation)
  • Problem-Solving and Analytical Skills: Data engineers must possess strong problem-solving abilities and the capacity to analyze complex technical challenges. They should be able to break down problems into manageable components and devise effective solutions
  • Software Product Development Lifecycle: Familiarity with the software development lifecycle (SDLC) is crucial. This includes understanding requirements gathering, system design, implementation, testing, deployment, and maintenance in an Agile/Scaled Agile manner. Experience with Scrum, Kanban, Extreme Programming, or other outcome based iterative development approach required
  • Knowledge of Development Tools and Frameworks: Data engineers should be proficient in using development tools and frameworks relevant to their domain. This can include version control systems (e.g., Git), integrated development environments (e.g., Visual Studio Code, IntelliJ), and frameworks specific to data platform development
  • Collaboration and Communication: Effective collaboration with cross-functional teams is vital for data engineers. Strong communication skills, both written and verbal, enable them to clearly express ideas, collaborate with colleagues, and convey technical concepts to non-technical stakeholders
  • Continuous Learning: The field of software engineering is constantly evolving, so a mindset of continuous learning is crucial. Staying updated with new technologies, programming languages, frameworks, and industry trends is highly valued
  • Testing and Debugging: Proficiency in automated software testing techniques, including unit testing, integration testing, and debugging, is important for ensuring the reliability and quality of software applications
  • Knowledge of Security Best Practices: Strong understanding of secure coding practices and the ability to apply them effectively in software development. Ability to implement security controls, conduct code reviews, and perform security-focused testing, ensuring adherence to industry standards and minimizing the risk of potential exploits
  • Compliance: Familiarity with regulatory compliance requirements and industry-specific security standards, such as GDPR, HIPAA, PCI-DSS, and ISO 27001. Ability to design and implement software solutions that meet these compliance standards, ensuring the protection of sensitive data and maintaining regulatory compliance
  • System Design and Architecture: Data engineers should have a solid understanding of data platform system design principles and architecture patterns. This includes scalability, performance optimization, and the ability to design robust and efficient software systems
  • Adaptability and Flexibility: Data engineers often encounter changing requirements, tight deadlines, and evolving technologies. Being adaptable, flexible, and able to quickly learn and adapt to new tools and frameworks is crucial

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