Graduate Data Engineer

About the Role

• Full-time, on-site opportunity based in Morena, Madhya Pradesh, India.
• Designed for fresh graduates and early-career professionals interested in data engineering.

Key Responsibilities

• Work closely with software, IT, analytics, operations, production, supply chain, finance, sales, and other business teams.
• Collect, process, transform, validate, integrate, and organize data for reporting, analytics, dashboards, automation, enterprise applications, and business decision-making.
• May involve working with various data engineering technologies depending on project and business requirements.

Requirements & Tech Stack

• Basic to good knowledge of Python and SQL.
• Familiarity with Python libraries such as Pandas, NumPy, Requests, SQLAlchemy, or similar data-processing libraries will be beneficial.
• Understanding of SQL queries, joins, aggregations, subqueries, views, indexes, transactions, stored procedures, and relational database concepts.
• Familiarity with MySQL, PostgreSQL, SQL Server, Oracle, SQLite, or similar relational database-management systems will be beneficial.
• Basic understanding of data cleaning, transformation, validation, profiling, standardization, deduplication, and data-quality concepts.
• Exposure to ETL/ELT processes, data pipelines, batch processing, data integration, workflow automation, or data migration will be advantageous.
• Familiarity with REST APIs, JSON, CSV, Excel, flat files, databases, and other common data sources will be beneficial.
• Basic understanding of data warehouses, data lakes, data marts, dimensional modelling, fact tables, dimension tables, and analytical data structures will be advantageous.
• Exposure to AWS, Microsoft Azure, or Google Cloud data services will be considered an advantage but is not mandatory.
• Familiarity with technologies such as Amazon S3, Redshift, Glue, Azure Data Factory, Synapse Analytics, Google BigQuery, or similar cloud data services will be beneficial.
• Exposure to Apache Spark, PySpark, Apache Airflow, Kafka, dbt, Hadoop, Databricks, or similar data-engineering tools will be advantageous but is not mandatory.
• Basic understanding of batch processing, streaming data, pipeline scheduling, orchestration, monitoring, and data-workflow concepts will be beneficial.
• Exposure to Power BI, Tableau, Looker, Excel, dashboards, reporting systems, or business-intelligence tools will be considered an additional advantage.
• Familiarity with Git, GitHub, version control, branching, pull requests, documentation, and collaborative development workflows.
• Exposure to Docker, Linux, command-line tools, CI/CD, or cloud deployment concepts will be beneficial but is not mandatory.
• Familiarity with data governance, data security, access control, data privacy, backup, recovery, and data-quality practices will be advantageous.
• Exposure to machine learning, artificial intelligence, generative AI, predictive analytics, or data-science workflows will be beneficial but is not mandatory.
• Exposure to ERP, CRM, manufacturing systems, supply-chain platforms, inventory systems, finance systems, or enterprise-data environments will be considered an advantage.
• Familiarity with Agile, Scrum, Jira, SDLC, technical documentation, requirements gathering, issue tracking, or project collaboration will be beneficial but is not mandatory.
• Good analytical, numerical, logical, troubleshooting, and problem-solving skills.
• Good communication, documentation, collaboration, and teamwork abilities.
• Ability to understand business and data requirements and contribute to reliable, structured, scalable, and maintainable data solutions.
• Willingness to work in an on-site environment.
• Internship, academic project, SQL project, Python project, ETL project, data-pipeline project, analytics project, database project, dashboard project, GitHub project, hackathon, freelance assignment, startup project, or open-source contribution will be considered but is not mandatory.
• Strong willingness to learn new data-engineering tools, cloud platforms, databases, pipeline technologies, analytics systems, and modern data practices.

Qualifications

• B.E. / B.Tech / B.Sc. / BCA / MCA / M.Sc. / M.Tech in Computer Science, Information Technology, Software Engineering, Computer Applications, Data Science, Artificial Intelligence, Mathematics, Statistics, or a related discipline.
• Fresh graduates and candidates with up to 2 years of relevant experience are strongly encouraged to apply.
• Final-year students completing their degree and available for full-time employment may also apply.
• Candidates with 0–2 years of experience in data engineering, data analytics, database development, ETL development, business intelligence, software development, data science, or related technology roles can apply.
• Candidates currently working as Graduate Data Engineer, Entry Level Data Engineer, Junior Data Engineer, Associate Data Engineer, Data Engineer Trainee, Graduate Engineer, ETL Developer, SQL Developer, Database Developer, Data Analyst, BI Analyst, Junior Data Analyst, Associate Data Analyst, Associate Software Engineer, or similar roles are encouraged to apply.
• Candidates from IT services, SaaS, product companies, technology, consulting, e-commerce, fintech, telecom, analytics, manufacturing, logistics, FMCG, or other industries are welcome.