AI Engineer

Dharampal Satyapal Group (DS Group); Dadri; On-site; INR 350,000 - 550,000; Full time

Job summary

Dharampal Satyapal Group (DS Group) is seeking a Graduate Trainee / Junior Data & Analytics Engineer. Fresh graduates with solid SQL and Python skills will build data pipelines, transform data, and develop dashboards to support business decisions.

The role involves working with experienced teammates to validate data, automate tasks, and deliver reliable reporting. Academic projects, internships, or certifications are welcome as evidence of capability.

Qualifications

• Fresh graduates with strong SQL fundamentals and Python programming ability.
• Academic projects, internships or certifications demonstrating data-engineering or analytics skills.
• Understanding of how data moves from operational systems to reporting and analytics.
• Ability to build pipelines, validate data, and create dashboards using Python-based tools.

Responsibilities

• Translate business requirements into data fields, mappings, validation rules and outputs.
• Write efficient SQL queries for extraction, aggregation, and reporting.
• Help build and maintain data pipelines between databases, files, APIs and data warehouses.
• Use Python to extract, clean, transform, load structured data.
• Develop reusable Python scripts and automation for recurring tasks.
• Create dashboards using Python viz tools or BI platforms and explain data logic.

Skills

SQL

Python

Data Pipelines

Dashboards

Experimentation

Education

BE/B.Tech

BCA

MCA

BSc/MSc

Tools

Pandas

NumPy

Streamlit

Power BI

Job description

Job Title: Graduate Trainee / Junior Data & Analytics Engineer

Employment Level: Fresher / Entry Level

ROLE PURPOSE

We are looking for a curious, technically strong fresher who wants to build a career across data engineering, analytics, database management, and dashboard development. The role is suitable for recent graduates with excellent SQL fundamentals, strong Python programming ability, and a clear understanding of how data moves from operational systems into reporting and analytical solutions.

The selected candidate will work with experienced team members to build reliable data pipelines, validate and transform data, create dashboards, automate recurring activities, and help business teams make better decisions. Prior full-time work experience is not required; however, candidates must be able to demonstrate their knowledge through academic projects, internships, personal projects, certifications, or a practical technical assessment.

KEY RESPONSIBILITIES

• Understand business requirements and convert them into clear data fields, source-to-target mappings, transformation rules, validation checks, and expected outputs.
• Write accurate and efficient SQL queries for data extraction, joining, aggregation, reconciliation, analysis, and reporting.
• Assist in building and maintaining data pipelines that move data between operational databases, files, APIs, data warehouses, and analytical platforms.
• Use Python to extract, clean, transform, validate, and load structured data.
• Develop reusable Python scripts and small applications for automation, reporting, data-quality checks, and recurring analytical tasks.
• Build clear and user-friendly dashboards using Python-based visualization or dashboard frameworks and, where required, standard business-intelligence tools.
• Analyse data to identify trends, exceptions, missing values, duplicates, inconsistent master data, and unusual business movements.
• Reconcile dashboard and report outputs with source data before release.
• Support incremental loads, scheduled jobs, error handling, logging, monitoring, and recovery of failed data-processing tasks.
• Document database objects, pipeline logic, field definitions, business rules, assumptions, dependencies, and refresh schedules.
• Work with business users, analysts, and technology teams to troubleshoot data issues and improve the reliability of reporting.
• Follow secure coding, data-access, version-control, testing, and documentation practices.
• Continuously learn new data technologies and contribute ideas for improving automation, performance, and user experience.

MANDATORY TECHNICAL KNOWLEDGE

1. SQL and database fundamentals

• Very strong command of SQL, including joins, subqueries, common table expressions, window functions, grouping, conditional aggregation, set operations, date functions, and data-cleaning logic.
• Ability to solve SQL problems involving duplicates, nulls, changing records, mismatched data types, multiple levels of granularity, and incomplete source data.
• Sound knowledge of relational database concepts, tables, keys, constraints, relationships, normalization, denormalization, views, indexes, and transactions.
• Basic understanding of query execution plans and the factors that affect database and query performance.
• Ability to design a simple database schema and explain why particular tables, keys, relationships, and indexes were selected.
• Strong interest in database management, data accuracy, data lineage, and source-of-truth controls.

2. Python programming

• Strong programming fundamentals in Python, including functions, classes, modules, collections, file handling, exception handling, and reusable code design.
• Hands-on knowledge of data-processing libraries such as pandas and NumPy.
• Ability to connect Python applications to databases, execute parameterized queries, process files, and consume REST APIs.
• Understanding of logging, debugging, configuration management, virtual environments, testing, and basic performance considerations.
• Ability to write readable, modular, and documented code instead of one-time scripts.
• Exposure to Python-based dashboards or visualisation tools such as Streamlit, Plotly, Dash, Matplotlib, Seaborn, or similar frameworks.

3. Data-pipeline understanding

• Clear understanding of ETL/ELT concepts and the stages involved in extracting, validating, transforming, and loading data.
• Knowledge of batch processing, incremental loading, scheduling, dependencies, error handling, retries, audit fields, and reconciliation.
• Ability to explain how a data pipeline should prevent duplicate loads, identify failed records, and prove that the target data matches the source.
• Familiarity with data warehouses, analytical databases, data lakes, or modern data platforms is useful.
• Awareness of orchestration, monitoring, and data-quality concepts will be an advantage.

4. Dashboard and analytical thinking

• Ability to choose appropriate charts, filters, KPIs, drill-downs, and layouts for a business question.
• Strong numerical and logical reasoning with the ability to understand what a metric means and why it may have changed.
• Ability to build simple interactive dashboards using Python and explain the data and calculation logic behind them.
• Exposure to any established business-intelligence or reporting tool is useful, but strong Python, SQL, and data understanding are the priority.
• Good attention to usability, performance, accuracy, and clarity when presenting data.

QUALIFICATIONS

• B.E./B.Tech, BCA, MCA, B.Sc./M.Sc., or equivalent qualification in Computer Science, Information Technology, Data Science, Statistics, Mathematics, Engineering, or a related discipline.
• Fresh graduates and candidates with internship experience may apply.
• Academic projects, personal projects, coding profiles, dashboards, database designs, or data-pipeline demonstrations will be considered as valuable evidence of capability.
• Relevant certifications are welcome but will not replace practical knowledge.

WHAT WE VALUE

• Strong problem-solving ability and willingness to investigate data instead of accepting results without validation.
• Curiosity about business processes and the ability to ask clear questions.
• Ownership, discipline, attention to detail, and a learning mindset.
• Clear written and verbal communication.
• Ability to explain technical work in simple language and collaborate respectfully with both technical and non-technical colleagues.

SELECTION APPROACH

The selection process may include a practical assessment covering advanced SQL, database design, Python programming, data transformation, pipeline logic, data validation, and dashboard creation. Candidates may also be asked to explain one academic, internship, or personal project in detail, including the problem, data model, code, testing approach, and final outcome.