Associate Data Engineer

About the Role

Blue Data Consulting is seeking a motivated and detail-oriented Associate Data Engineer to join their data team. This role is designed for recent graduates or early-career professionals with up to 1 year of experience. You will work on real-world projects alongside experienced engineers, gaining hands-on experience in data pipeline development, large-scale data processing, and cloud-based data platforms. The role includes structured mentorship and a growth path towards becoming a Data Engineer.

Key Responsibilities

• Data Processing and Pipelines
• Write clean, well-structured Python scripts for data processing, transformation, and automation.
• Assist in building and maintaining ETL/ELT pipelines and data workflows.
• Work with structured, semi-structured (JSON, CSV, Parquet), and unstructured data from multiple sources (APIs, databases, files).
• Perform data cleaning, transformation, and validation using libraries such as Pandas or Polars.
• Databases and SQL
• Write and optimize SQL queries for data extraction, analysis, and transformation.
• Work with relational databases such as PostgreSQL, MySQL, or SQL Server.
• Assist in designing simple data models and database tables.
• Data Quality and Support
• Help implement data quality checks for accuracy and consistency.
• Monitor scheduled jobs and assist in troubleshooting pipeline failures and data issues.
• Support data integration activities across systems and teams.
• Cloud and Tools Exposure
• Gain hands-on exposure to cloud data services on AWS, Azure, or GCP (e.g., S3, Azure Data Factory, BigQuery).
• Learn and work with orchestration tools such as Apache Airflow and transformation tools such as dbt.
• Get introduced to modern data platforms such as Databricks, Snowflake, or Microsoft Fabric.
• Collaboration and Learning
• Work closely with senior team members to understand requirements and deliver tasks.
• Participate in code reviews, team discussions, and knowledge-sharing sessions.
• Maintain clear documentation of data processes, pipelines, and technical work.
• Continuously learn new tools, frameworks, and best practices in data engineering.

Requirements & Tech Stack

• Strong understanding of Python fundamentals (data structures, functions, file handling, error handling).
• Good working knowledge of SQL (joins, aggregations, GROUP BY, subqueries).
• Basic understanding of relational databases and data modeling concepts.
• Basic understanding of data processing concepts and ETL/ELT workflows.
• Familiarity with working with data formats such as CSV, JSON, and Excel.
• Familiarity with Git and GitHub.
• Good analytical skills and attention to detail.
• Clear written and verbal communication.
• A strong willingness to learn and adapt quickly.

Good to Have

• Hands-on experience with Pandas, NumPy, or Polars.
• Knowledge of advanced SQL concepts (window functions, CTEs).
• Basic exposure to Apache Spark / PySpark.
• Familiarity with Airflow, dbt, or similar data tools.
• Basic exposure to a cloud platform (AWS, Azure, or GCP).
• Basic understanding of data warehousing concepts.
• Familiarity with NoSQL databases such as MongoDB.
• Basic knowledge of Docker or Linux commands.
• Exposure to BI tools such as Power BI or Tableau.
• Relevant certifications (e.g., AWS Cloud Practitioner, Azure Data Fundamentals DP-900, Databricks Fundamentals), Kaggle work, or hackathon participation.
• A GitHub profile or portfolio showcasing data or Python projects.

Qualifications

• Bachelor’s degree in Computer Science, Information Technology, Data Science, Engineering, or a related technical field.
• Academic, internship, or personal projects involving Python, SQL, databases, data engineering, or cloud technologies will be strongly considered.
• Strong fundamentals, a solid educational background, and a logical, problem-solving mindset.
• Candidates who can clearly explain their academic or personal projects (data, tools, learnings).
• Curiosity, attention to detail, and the ability to learn quickly.
• Passion for working with data.