Data Engineer
First seen by Alion on Aug 21, 2026.
About the Role :
We're looking for a Data Engineer with 1 - 2 years of experience to join our data team. You'll help build, maintain, and optimize data pipelines that power analytics and decision-making across the organization. This is a great opportunity for someone early in their career who wants to deepen their skills in workflow orchestration, data pipeline design, and Python-based data engineering.
Key Responsibilities :
- Design, build, and maintain ETL/ELT data pipelines using Apache Airflow.
- Write clean, efficient, and reusable Python code for data processing and transformation.
- Use Jupyter Notebooks for data exploration, prototyping, and validation of pipeline logic.
- Monitor pipeline performance, troubleshoot failures, and ensure data quality and reliability.
- Collaborate with data analysts, data scientists, and other engineers to understand data requirements.
- Maintain and improve existing DAGs, including scheduling, dependency management, and error handling.
- Document pipelines, workflows, and processes for team-wide visibility.
- Assist in optimizing SQL queries and database performance where needed.
Required Skills & Qualifications :
- 1 - 2 years of hands-on experience in data engineering or a related role.
- Proficiency in Apache Airflow (DAG creation, scheduling, operators, task dependencies).
- Strong working knowledge of Python for scripting and data manipulation.
- Experience using Jupyter Notebook for data analysis/prototyping.
- Familiarity with SQL and relational databases.
- Understanding of ETL/ELT concepts and data pipeline best practices.
- Basic knowledge of version control (Git).
Nice to Have :
- Experience with cloud platforms (AWS / GCP / Azure).
- Familiarity with data warehousing tools (Snowflake, BigQuery, Redshift, etc.).
- Exposure to libraries like Pandas, NumPy, or PySpark.
- Understanding of data modeling concepts.
- Experience with containerization (Docker).
Skills
Data Engineering, Python, Apache Airflow, SQL, Data Management, Pandas, Numpy, Spark