Job VC

TJHelpers
is committed to developing strong engineering teams through mentorship, real project experience, and our “Helpers as a Service” model. We provide structured growth, hands-on practice, and a supportive environment where engineers can evolve quickly and meaningfully.

We are looking for a Junior Data Engineer to join our team and help build and maintain reliable data pipelines, transformations, and datasets for modern products in fintech, AI, telecommunications, healthcare, and other data-driven domains.
This is a great opportunity to work with real-world data, grow in data engineering, and gain hands-on experience with SQL, Python, ETL/ELT processes, and modern data platforms.

Responsibilities
Support the development and maintenance of data pipelines and ETL/ELT processes
Collect, clean, transform, and load data from databases, APIs, files, and external systems
Write and optimize SQL queries for data preparation and transformation
Assist in building datasets for analytics, reporting, and internal products
Support data quality, consistency, and reliability across pipelines and datasets
Work with structured and semi-structured data
Participate in troubleshooting and improving data processes
Assist in monitoring and improving data pipeline reliability and performance
Collaborate with engineers, analysts, and product teams

Requirements
Basic understanding of data engineering concepts and data pipeline architecture
Hands-on experience writing SQL queries and working with Python
Understanding of relational databases such as PostgreSQL or MySQL
Ability to write SQL queries using joins, filtering, aggregations, and transformations
Basic understanding of ETL/ELT processes
Understanding of data modeling and data transformation principles
Familiarity with data formats such as CSV and JSON and working with APIs
Basic understanding of structured and semi-structured data
Analytical thinking and attention to detail
Motivation to learn and grow in a technical environment

Nice to Have
Basic experience with Pandas
Basic familiarity with Spark or PySpark
Familiarity with Airflow, dbt, or similar tools
Understanding of data warehouses such as BigQuery, Snowflake, or Redshift
Familiarity with cloud platforms such as AWS, GCP, or Azure
Familiarity with Git
Experience with BI or analytics tools
Interest in data platforms, analytics engineering, or ML/AI workflows

What We Offer
Work with real production data systems and modern technologies
Mentorship from experienced engineers and tech leads
Clear growth path to Middle Data Engineer level
Supportive engineering culture focused on learning and practical development
Flexible schedule and fully remote work opportunities