[Hiring] Junior Data Engineer @EdgeQ Technologies

Role Description

We are seeking a motivated and detail-oriented Junior Data Engineer to join our data engineering team. The ideal candidate will have a foundational understanding of Python, SQL, ETL processes, and Apache Spark, with a strong interest in building reliable, scalable data pipelines.

In this role, you will help develop and maintain data ingestion workflows, transform raw data into usable datasets, and support data integration initiatives. You will collaborate with data engineers, data analysts, data scientists, and other technical teams to ensure data is accurate, accessible, and ready for reporting and analytics.

This opportunity is ideal for recent graduates and early-career professionals who want to build their skills in data engineering, big data processing, and modern data platforms.

Key Responsibilities

• Develop, test, and maintain data pipelines using Python and SQL.
• Assist in building and managing ETL (Extract, Transform, Load) workflows.
• Extract data from databases, APIs, files, and other source systems.
• Clean, transform, validate, and load data into target systems.
• Use Apache Spark for distributed data processing and large-scale data transformations.
• Write efficient SQL queries to retrieve, join, aggregate, and manipulate datasets.
• Monitor data pipelines and troubleshoot failures, performance issues, and data quality problems.
• Support the development of batch-processing and, where applicable, streaming data workflows.
• Implement data validation checks to maintain data accuracy and consistency.
• Collaborate with analytics and business intelligence teams to support reporting and analytical requirements.
• Document pipeline logic, data sources, transformations, and technical procedures.
• Follow coding standards, version control practices, and data security guidelines.
• Participate in code reviews, testing, and continuous improvement initiatives.

Qualifications

• Bachelor's degree in Computer Science, Information Technology, Data Engineering, Software Engineering, or a related field, or equivalent practical experience.
• 0–3 years of experience in data engineering, software development, data analytics, or a related technical field.
• Working knowledge of Python for scripting, data processing, and automation.
• Proficiency in SQL, including joins, aggregations, subqueries, and data manipulation.
• Understanding of ETL/ELT concepts and data pipeline development.
• Basic familiarity with Apache Spark and distributed data processing.
• Knowledge of relational databases and data modeling fundamentals.
• Understanding of data cleaning, validation, and transformation techniques.
• Familiarity with Git or other version control tools.
• Strong analytical thinking, problem-solving, and debugging skills.
• Good written and verbal communication skills.

Preferred Qualifications

• Exposure to PySpark for large-scale data transformations.
• Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
• Basic knowledge of data warehouses such as Snowflake, Amazon Redshift, or Google BigQuery.
• Understanding of data lake and lakehouse architectures.
• Familiarity with workflow orchestration tools such as Apache Airflow.
• Exposure to Docker, CI/CD, or automated testing.
• Understanding of batch processing, incremental loads, and data pipeline scheduling.
• Familiarity with dimensional modeling and data warehouse design.
• Academic projects, internships, or personal projects involving data pipelines or big data technologies.

Requirements

• Must currently reside in the United States.
• Applicants must be eligible to work in the United States under the employer's applicable hiring requirements.
• Remote employment is subject to state-specific payroll eligibility and company policies.
• Some positions may require occasional office attendance or collaboration during U.S. business hours.

Salary and Benefits

• Annual base salary: $75,000–$115,000 USD.
• Full-time employment.
• Remote, hybrid, or on-site work arrangements, depending on the position.
• Potential benefits may include health insurance, retirement plans, paid time off, and professional development opportunities, depending on the employer.
• Opportunities to develop skills in cloud data engineering, distributed computing, and scalable data architecture.
• The salary range is indicative and not a verified offer. Actual compensation may vary by employer, experience, location, and role requirements.

Work Locations

• Seattle, Washington
• Austin, Texas
• Dallas, Texas
• New York City, New York
• Atlanta, Georgia

How to Apply

Interested candidates should submit an updated résumé highlighting their Python, SQL, ETL, and Spark skills. Applicants are encouraged to include internships, academic projects, GitHub repositories, or portfolio examples demonstrating data pipeline development and transformation experience.

Please include your current U.S. city and state in your application to confirm location eligibility.