Junior Data Engineer

We are looking for a curious and driven Junior Data Engineer to join our growing data engineering team in Gurugram. This role is ideal for fresh graduates from good engineering colleges who have a solid foundation in programming and databases and a genuine interest in data. You will work on building and maintaining ETL pipelines, designing data models, and contributing to enterprise-wide data ontology initiatives - learning to not just move data, but truly understand and represent it meaningfully across business domains.

The core responsibilities for the job include the following:

ETL Pipeline Development:

• Design, develop, and maintain ETL/ELT pipelines to extract data from diverse sources, including databases, APIs, flat files, and cloud storage.
• Work with data processing frameworks such as Apache Spark, Apache Airflow, or AWS Glue for pipeline orchestration and scheduling.
• Ensure data quality, consistency, and completeness through validation rules, reconciliation checks, and robust error-handling mechanisms.
• Implement data lineage tracking to maintain a clear audit trail of data movement and transformations across systems.
• Optimize pipeline performance for large-scale data loads and incremental data processing scenarios.

Data Modeling:

• Design conceptual, logical, and physical data models aligned to business domains and enterprise data standards.
• Develop and maintain dimensional models (star schema, snowflake schema) for analytical workloads and data warehouse layers.
• Collaborate with data architects and business analysts to translate business requirements into well-structured data schemas.
• Document data models, entity-relationship diagrams (ERDs), and schema definitions clearly for reuse across teams.
• Support the implementation of master data management (MDM) strategies and entity resolution approaches.

Data Ontology and Domain Understanding:

• Develop and maintain data ontologies and taxonomies to standardize domain vocabulary, entity definitions, and relationships across enterprise systems.
• Work with domain experts across finance, HR, operations, and other business functions to formalize business concepts into structured ontological representations.
• Contribute to the development of knowledge graphs and semantic data layers using tools such as Neo4j or RDF-based frameworks.
• Support data cataloging and metadata management initiatives to improve data discoverability and governance across the organization.
• Participate in defining controlled vocabularies and business glossaries that bridge technical data structures with business language.

Collaboration and Delivery:

• Partner with data analysts, BI engineers, and data scientists to understand downstream data consumption requirements.
• Participate in Agile ceremonies, including sprint planning, daily standups, and retrospectives.
• Write clean, well-documented Python and SQL code following team coding standards and best practices.
• Contribute to internal knowledge sharing and documentation of data engineering standards.

Requirements:

• Experience Level: Freshers / 0-1 Year.
• B. Tech / B. E. in Computer Science, Information Technology, Electronics, or a related engineering discipline.
• Proficiency in Python for data manipulation and scripting.
• Strong SQL skills - query writing, joins, aggregations, and window functions.
• Basic understanding of ETL concepts, data pipelines, and data flow architectures.
• Familiarity with relational databases (PostgreSQL, MySQL) and data modeling fundamentals.
• Understanding of data ontology concepts - entities, relationships, and taxonomies (formal coursework or self-study accepted).

Good to Have:

• Exposure to cloud platforms: AWS (S3 Glue, Redshift), GCP (BigQuery, Dataflow), or Azure (Data Factory, Synapse).
• Familiarity with Apache Spark, Airflow, or Pandas for data processing.
• Basic knowledge of graph databases (Neo4j).
• Understanding of data governance and data cataloguing concepts.

Soft Skills:

• Analytical mindset with strong problem-solving ability.
• Eagerness to learn and adapt in a fast-paced data-driven environment.
• Good written and verbal communication skills.
• Curiosity-driven learner who proactively stays ahead of the rapidly evolving AI landscape.
• Ability to collaborate effectively across technical and business teams.