Junior Data Engineer (Remote)
The Junior Data Engineer at Confidential Client is an entry-level role focused on developing and maintaining scalable data pipelines using Python, SQL, and Apache Spark. The position involves collaborating with cross-functional teams to ensure data accuracy and accessibility for analytics, supporting ETL workflows, and contributing to data integration and transformation initiatives. This role offers opportunities to build foundational skills in modern data engineering, big data processing, and cloud platforms within a flexible remote or hybrid work environment.
About Careertakes
👉 Important disclosure: Careertakes is a third-party recruiting platform supporting this hiring process. If selected, you will be employed directly by our client, a company in the IT industry.
Applicants for this role may also receive access to additional matched opportunities through the Careertakes platform.
About the Role
Confidential Client is hiring a Junior Data Engineer to help build and maintain reliable, scalable data pipelines. This entry-level role is ideal for recent graduates and early-career technologists who want hands-on experience with Python, SQL, ETL processes, and Apache Spark in a production environment.
You will work with engineers, analysts, and data scientists to ingest, transform, validate, and deliver data for reporting and analytics. Remote work is available where permitted by employer payroll and hiring policies.
What You’ll Do
- Develop, test, and maintain data pipelines using Python and SQL
- Assist in designing and operating ETL/ELT workflows to ingest data from databases, APIs, and files
- Clean, transform, validate, and load datasets into target systems
- Use Apache Spark (and PySpark where applicable) for distributed processing of large datasets
- Write efficient SQL for joins, aggregations, and data manipulation
- Monitor pipelines, troubleshoot failures, and resolve performance or data quality issues
- Support both batch-processing and, when applicable, streaming workflows
- Implement automated data validation and alerting to preserve data integrity
- Document pipeline logic, transformations, data sources, and runbooks
- Follow version control, code-review, and data security best practices
Required Qualifications
- Bachelor’s degree in Computer Science, Data Engineering, Software Engineering, Information Technology, or equivalent practical experience
- 0–3 years of hands-on experience in data engineering, software development, or data analytics
- Working knowledge of Python for scripting, data processing, and automation
- Proficiency with SQL (joins, aggregations, subqueries, data manipulation)
- Understanding of ETL/ELT concepts and pipeline development
- Basic familiarity with Apache Spark and distributed data processing
- Knowledge of relational databases and data modeling fundamentals
- Familiarity with Git or other version control systems
- Strong analytical thinking, debugging, and problem-solving skills
- Good written and verbal communication skills
- Must currently reside in the United States
Preferred Qualifications
- Exposure to PySpark for large-scale transformations
- Experience or coursework with cloud platforms (AWS, Azure, or GCP)
- Familiarity with modern data warehouses (Snowflake, Redshift, BigQuery)
- Understanding of data lake / lakehouse architectures
- Experience with workflow orchestration tools (Apache Airflow)
- Exposure to Docker, CI/CD, or automated testing practices
- Knowledge of dimensional modeling and scheduling incremental loads
- Academic projects, internships, or portfolio examples showing data pipeline work (e.g., GitHub)
Technical Skills
- Programming: Python (required)
- Database querying: SQL (required)
- Data integration: ETL / ELT pipelines
- Big data processing: Apache Spark / basic PySpark
- Databases: relational database concepts; familiarity with cloud data warehouses (preferred)
- Version control: Git, GitHub or GitLab
- Cloud (preferred): AWS, Azure, or Google Cloud Platform
- Orchestration (preferred): Apache Airflow
- Core competencies: debugging, data quality monitoring, and problem-solving
Salary & Benefits
- Annual base salary: $75,000–$115,000 USD
- Full-time employment
- Remote, hybrid, or on-site arrangements depending on role and payroll eligibility
- Typical benefits may include health insurance, retirement plans, paid time off, and professional development (varies by employer)
- Opportunities to develop skills in cloud data engineering, distributed computing, and scalable architecture
Work Location & Eligibility
- Authoritative listing location: International, WA (Telecommute/Remote-eligible)
- Candidates must currently reside in the United States and be eligible to work under the employer’s hiring and payroll policies
- Remote employment may be limited by state payroll rules; some roles may require occasional overlap with U.S. business hours or onsite collaboration
Equal Opportunity & Hiring Transparency
Careertakes and our client are Equal Opportunity Employers committed to building a diverse and inclusive workforce. We prohibit discrimination or harassment of any kind. To support a fair and efficient hiring process, AI tools may be used to assist with application review or resume screening. These tools do not replace human decision-making. Final hiring decisions are made by people.
If you have questions about how your data is used, please contact us directly.
Negotiate a higher salary! Check the salary ranges for this job type in your area.
View My Salary Range