Data Engineer I

Inspira Financial•Oak Brook, IL

42d

About The Position

We are seeking a talented entry level Data Engineer who can create scalable data platform solutions while collaborating with an experienced team of product development professionals. You will join a cross-functional, DevOps based, Agile team responsible for the entire product development life cycle, encompassing conception, discovery, framing, development, deployment, measurement, and continuous improvement. As a Data Engineer, you should possess expertise in modern data-centric coding languages, development frameworks, and third-party libraries. Additionally, we value individuals who are team players, possess a keen eye for visual design, and prioritize outcomes over outputs. 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! At Inspira, we believe in empowering engineers to build smarter platforms - one where human expertise is amplified, not replaced, by AI tools. We seek engineers who have demonstrated experience using AI responsibly to accelerate their development workflow, improve code quality, and solve complex problems more efficiently. The Data Engineer I will report to the Data Engineering Manager in the Data Engineering Team.

Requirements

0-2 years of experience in Data Engineering.
Bachelor’s degree preferred in Computer Science, Computer Engineering, Software Engineering, Statistics, Data Science, or similar/related Engineering/Science based disciplines.
Microsoft Certified Azure Data Fundamentals
Snowflake SnowPro Certification
Snowflake SnowPro Certification Data Engineer
Strong Programming Skills: Proficiency in programming languages such as SQL/T-SQL, Python is essential. Data engineers should have a deep understanding of data structures, algorithms, and the ability to write clean, efficient, and maintainable code.
Data / Database Skills: Knowledge and competence with relational and noSQL databases (e.g., SQL Server, MongoDB) including proficiency with Data Definition Languages, Data Mark-Up Languages and ability to design and create database tables, views, functions and stored procedures
Design and implement OLAP databases to serve internal and external consumer use cases
Design and implement resilient and performant data pipeline and transformation processes
Design and implement hybrid data cloud services, leveraging public could providers (i.e. Azure) and specialty providers (i.e. Snowflake)
Cloud Skills: Experience working in Hybrid and Cloud based environments where essentially all things are handled “as code” and promoted via automated pipelines which incorporate audit, quality, and security controls.
Support the development of enterprise data warehouse strategy ensuring rapid delivery while taking responsibility for applying standards, principles, theories, and concepts
Support enterprise data governance initiatives, and drive efforts in defining data standards, designing classification taxonomies, developing data management processes, ensuring proper meta-data management
Work cross-functionally with other internal business units, such as marketing and operations
Data Engineering Tools/Platforms o Platform/Framework (Snowflake, Azure MSSQL) o ETL (Qlik Replicate, Talend, SSIS) o Job Orchestration (ActiveBatch, Tidal, Airflow) o Entity Data Modeling (ER/Studio, Erwin)
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.

Nice To Haves

Strong experience with advanced analytics tools for Object-oriented/object function scripting using languages such as Python
Strong experience in working with and optimizing existing ETL processes and data integration and data preparation flows and helping to move them in production
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

Responsibilities

Collaborating with Users, Engineers, Designers, and Product Managers to foster ideation and deliver cutting-edge software solutions.
Employing Test First and Acceptance Criteria Driven approaches to continuously test and deliver high-quality software.
Designing and developing data pipelines while adhering to industry best practices and standards.
Ensuring solutions meet non-functional requirements, such as security, performance, maintainability, scalability, usability, and reliability.
Develop and maintain basic Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) data pipelines in Talend.
Write and optimize SQL queries for data analysis, data extraction, data manipulation and writing and modifying existing SQL queries for better performance in Snowflake.
Clean, validate, and transform raw data to ensure quality and integrity.
Learn and apply techniques in evolving technologies such as Generative and Agentic AI
Train and assist with data modeling and the management of databases, such as relational and NoSQL databases.
Help troubleshoot issues with data infrastructure and pipelines as and when Prod support schedules require to be on call rotation.
Contribute to documenting data workflows and best practices.
Perform performance tuning and monitoring of data processes.
Actively pursuing continuous professional growth by staying updated on new development tools, programming techniques, and computing equipment. Engaging in educational opportunities, keeping informed through professional publications, nurturing personal and professional networks, and actively participating in relevant professional organizations.
Through the successful execution of these responsibilities, the data engineer will make significant contributions to the overall achievements of our team and organization!