Support building and maintaining ETL/ELT data pipelines and cloud data workflows. Work with senior engineers and cross-functional teams to implement, test, deploy, and document data processes. Continuously learn cloud technologies, SQL, and Python/PySpark, follow coding best practices, and contribute to process improvements in an Agile environment.
Key Responsibilities
Data Engineering Support
- Assist in building and maintaining data pipelines, ETL/ELT processes, and cloud-based data workflows.
- Work closely with senior engineers to understand solution design, data architectures, and coding best practices.
- Participate in development, testing, and deployment activities under guidance.
Collaboration & Communication
- Work with cross-functional teams to gather requirements and understand business needs.
- Communicate progress, challenges, and technical concepts clearly to team members.
- Support documentation of data processes, workflows, and technical specifications.
Learning & Continuous Improvement
- Continuously enhance technical skills in cloud technologies, SQL, and programming.
- Adopt best practices in coding, testing, and version control.
- Contribute ideas for improving processes and overall solution quality.
Must-Have Skills
- Strong understanding of data engineering fundamentals, SQL, and data processing concepts.
- Basic knowledge of cloud services (AWS/Azure/GCP) and data tools.
- Familiarity with Python or PySpark for data manipulation.
- Solid problem-solving and analytical abilities.
- Eagerness to learn new technologies and work in an Agile environment.
Good communication and teamwork skills
ResponsibilitiesKey Responsibilities
Data Engineering Support
- Assist in building and maintaining data pipelines, ETL/ELT processes, and cloud-based data workflows.
- Work closely with senior engineers to understand solution design, data architectures, and coding best practices.
- Participate in development, testing, and deployment activities under guidance.
Collaboration & Communication
- Work with cross-functional teams to gather requirements and understand business needs.
- Communicate progress, challenges, and technical concepts clearly to team members.
- Support documentation of data processes, workflows, and technical specifications.
Learning & Continuous Improvement
- Continuously enhance technical skills in cloud technologies, SQL, and programming.
- Adopt best practices in coding, testing, and version control.
- Contribute ideas for improving processes and overall solution quality.
Must-Have Skills
- Strong understanding of data engineering fundamentals, SQL, and data processing concepts.
- Basic knowledge of cloud services (AWS/Azure/GCP) and data tools.
- Familiarity with Python or PySpark for data manipulation.
- Solid problem-solving and analytical abilities.
- Eagerness to learn new technologies and work in an Agile environment.
Good communication and teamwork skills
Qualifications- Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, or a related field.
Similar Jobs
.png)
Cloud • Insurance • Payments • Software • Business Intelligence • App development • Big Data Analytics
Design, build, and optimize BigQuery-based data solutions and data models. Manage data storage, partitioning, clustering, ETL, and data quality. Collaborate with architects, data scientists, and engineers to deliver scalable, secure cloud data infrastructure and documentation; senior hires provide technical leadership, code review, and process improvement.
Top Skills:
Ansi Sql)BigQueryConfluenceGoogle Cloud Platform (Gcp)JIRAPythonSql (Bigquery Sql
Artificial Intelligence • Fintech • Insurance • Marketing Tech • Software • Analytics
Design, build, and maintain scalable ETL pipelines and data models to support pricing and analytics. Perform complex data profiling and analysis, enforce data quality and governance, collaborate with data scientists and stakeholders, mentor junior engineers, and support product launches using Python, SQL, Snowflake, and cloud tooling.
Top Skills:
AWSBusiness Intelligence ToolsDatabricksDbtETLPythonSnowflakeSparkSQL
Artificial Intelligence • Fintech • Machine Learning • Mobile • Payments • Retail • Software
Own and modernize Upside s analytics data platform: migrate pipelines, reduce cost, improve governance, design reusable modeling/orchestration patterns, deliver domain-critical data products, lead cross-functional initiatives, mentor engineers, and support ML and product teams.
Top Skills:
AWSCi/CdDagsterDatabricksDbtSnowflakeTerraform
What you need to know about the Chicago Tech Scene
With vibrant neighborhoods, great food and more affordable housing than either coast, Chicago might be the most liveable major tech hub. It is the birthplace of modern commodities and futures trading, a national hub for logistics and commerce, and home to the American Medical Association and the American Bar Association. This diverse blend of industry influences has helped Chicago emerge as a major player in verticals like fintech, biotechnology, legal tech, e-commerce and logistics technology. It’s also a major hiring center for tech companies on both coasts.
Key Facts About Chicago Tech
- Number of Tech Workers: 245,800; 5.2% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: McDonald’s, John Deere, Boeing, Morningstar
- Key Industries: Artificial intelligence, biotechnology, fintech, software, logistics technology
- Funding Landscape: $2.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Pritzker Group Venture Capital, Arch Venture Partners, MATH Venture Partners, Jump Capital, Hyde Park Venture Partners
- Research Centers and Universities: Northwestern University, University of Chicago, University of Illinois Urbana-Champaign, Illinois Institute of Technology, Argonne National Laboratory, Fermi National Accelerator Laboratory
