Junior Data Engineer
First seen by Alion on Nov 6, 2026.
Role Summary
You build and maintain data pipelines for our clients - different industries, different stacks, one engineering standard. Python and SQL will be your daily tools; a mentor and code review will be your safety net. We hire data engineers, not software developers who occasionally work with data: fundamentals, the habit of asking "why", and a real grasp of what the craft is about. The rest we teach.
The Mission
Build pipelines that produce data people can trust: they run on schedule, fail loudly, and are easy to fix - regardless of the platform.
The Tech Stack
Core Languages: Python (clean, tested code), SQL (solid basics). Scala welcome.
Processing Frameworks: PySpark (concepts to basics), Pandas and Polars for local analysis.
Clouds (exposure to): AWS, GCP, or Azure - storage, compute, IAM.
Orchestration: Airflow, Dagster, or similar (concepts).
You will learn: dbt, Docker, CI/CD, Databricks/Snowflake, Terraform.
Your Responsibilities
Build and maintain ETL/ELT pipelines from APIs, databases, and files.
Validate your data before passing it downstream: row counts, nulls, duplicates.
Write tests, participate in code reviews - your code and others'.
Help troubleshoot and monitor pipelines in production.
Learn deliberately - dbt, Snowflake, Terraform - and share what you learn.
What We Offer:
Long-term career stability with a competitive salary paid in USD.
Conditions for steady career development.
Development supported by dedicated mentors and a variety of programs focused on expertise and innovation.
Private medical insurance provided after successful completion of the probationary period
A well-equipped and cozy office supports comfort and productivity across all project stages.
Welcoming atmosphere and a friendly corporate culture.
Requirements
Your Skills
Experience: 1+ year building software in Python.
Python Engineering: clean modular code, OOP basics, and a project set up from scratch: virtual environment, dependencies, sensible layout, pytest tests for your own code.
Git: daily fluency, not just familiarity: branches, commits, pull requests, review comments.
SQL: joins, aggregations, window functions; any relational database (PostgreSQL, MySQL, SQL Server).
DE Concepts: you chose data engineering deliberately. You can explain ETL vs ELT, what a warehouse is and how it differs from a database and a lake, and why pipelines must be idempotent.
Processing & Orchestration: you know why Spark and Airflow exist (distributed processing, DAGs). Concepts are required; basic hands-on with at least one of them is a strong plus. Pandas or Polars for local work is assumed.
Terminal: bash basics: navigate, grep logs, ssh.
Cloud: conceptual understanding of one major platform.
Language: English B2 or higher.
Nice to Have
Streaming: basic concepts (Kafka, Kinesis).
NoSQL: exposure to MongoDB or DynamoDB.
Formats: Parquet, Avro.
Containers: Docker basics.