Associate Software Engineer

This opportunity is particularly relevant for early-career engineers interested in building a career at the intersection of software engineering, data engineering, and business analytics. Candidates will gain exposure to technologies used to organize, transform, validate, and present data for business and clinical stakeholders. The position emphasizes strong SQL fundamentals, reliable data pipelines, structured data modeling, and collaborative software development practices. Applicants with Python programming experience and an understanding of modern ELT architectures can align their existing skills with the team's technical requirements.

What You'll Work On

The Associate Software Engineer will contribute to developing and maintaining data solutions that support reporting, analytics, and operational decision-making. The responsibilities combine database development, data transformation, workflow management, and data validation.

Key responsibilities include:

• Assist in designing and optimizing data models and core database schemas for structured data processing.

• Develop and maintain ELT transformation layers using dbt within Snowflake-based data architectures.

• Create, schedule, and monitor data workflows using Apache Airflow and related orchestration practices.

• Write efficient SQL queries and scripts for data extraction, transformation, aggregation, and analysis.

• Implement data quality checks, integrity validations, and unit testing configurations to improve data reliability.

• Develop dashboards and reporting experiences using Sigma, Tableau, Power BI, or comparable business intelligence platforms.

• Collaborate with Agile teams on development tasks, code reviews, and incremental improvements to CI/CD workflows.

• Translate business requirements into clear technical tasks and analytical solutions with guidance from experienced teammates.

• Help identify data discrepancies, investigate pipeline failures, and support the maintenance of reliable data processing workflows.

The role requires attention to detail and an understanding of how accurate, well-structured data supports business operations.

Technical Skills and Tools

Candidates should have a solid foundation in relational databases and SQL, together with an interest in modern data engineering platforms.

Snowflake, dbt, and Airflow are particularly relevant to the advertised position. Candidates who have worked with equivalent technologies should be prepared to explain how their knowledge transfers to the tools used by the team.

Who Can Apply

The position is intended for candidates with an undergraduate or postgraduate qualification in a relevant discipline and foundational technical experience.

Educational qualifications:

• B.Tech or B.E. in any specialization, with Computer Science preferred.

• Bachelor of Computer Applications (BCA).

• Master of Computer Applications (MCA).

Experience: 0–2 years in data engineering, software development, or relevant technical internships.

Applicants should demonstrate practical SQL knowledge, familiarity with relational databases, and an understanding of data processing workflows. Experience with cloud data warehouses, ELT pipelines, data modeling, or business intelligence tools can strengthen an application.

Candidates who have completed relevant academic projects, personal projects, or internships should highlight their contributions clearly. For example, a project that ingests raw data, transforms it into structured analytical tables, validates data quality, and produces reporting outputs can demonstrate several skills relevant to this position.

Understanding the Data Engineering Environment

The technologies listed in this job description serve different purposes within a modern data platform.

Snowflake is a cloud data warehouse used to store and process structured data for analytical workloads. Engineers working with Snowflake need to understand SQL-based transformations, warehouse objects, and efficient query execution.

dbt supports SQL-based data transformations and helps organize analytical models into maintainable workflows. It also provides capabilities for testing data assumptions and documenting transformation logic.

Apache Airflow is used to orchestrate workflows by defining task dependencies, schedules, and execution sequences. Engineers should understand how to monitor task runs, inspect failures, and manage dependencies between pipeline stages.

SQL and data modeling provide the foundation for building reliable analytical datasets. Candidates should understand primary keys, foreign keys, table relationships, normalization, and the difference between transactional and analytical data models.

Business intelligence tools, including Sigma and Power BI, help transform curated datasets into dashboards and reports that stakeholders can use to monitor business performance.

Understanding how these technologies work together will help candidates explain the purpose of each component during technical interviews.

What Recruiters May Evaluate

Candidates should be prepared to demonstrate practical problem-solving skills rather than relying exclusively on theoretical definitions.

SQL assessments may cover joins, grouping, subqueries, CTEs, window functions, duplicate identification, and handling missing values. Interviewers may also ask candidates to explain how they would investigate an incorrect aggregation or identify records that violate expected data relationships.

Data engineering discussions may focus on the differences between ETL and ELT, the purpose of data modeling, and methods for validating data after transformation. Candidates with dbt experience should understand models, tests, dependencies, and incremental transformations.

For Airflow-related discussions, useful preparation topics include DAGs, task dependencies, scheduling, retries, and failure monitoring. Applicants familiar with other orchestration platforms should explain their understanding of workflow scheduling and operational reliability.

Python may be relevant for data processing, validation scripts, API integration, and automation. Candidates should be comfortable explaining their code and describing how they test the correctness of its output.

Communication is also important. Engineers must be able to describe technical issues clearly, document their findings, and collaborate with teammates to resolve problems.

How to Strengthen Your Application

Candidates can improve their applications by presenting concrete evidence of their technical abilities.

A relevant portfolio project could demonstrate a complete data workflow: loading source files into a raw data layer, transforming the data into clean analytical tables, applying validation rules, and creating a dashboard for business reporting.

If you have used PySpark, Delta Lake, Databricks, or another data processing platform, explain the underlying engineering concepts and how your project demonstrates transferable skills. Do not list Snowflake, dbt, or Airflow as hands-on experience unless you have actually worked with them.

Your resume should prioritize SQL, database fundamentals, data modeling, ETL/ELT concepts, data quality checks, Python, and relevant project experience. Include measurable outcomes only when you can substantiate them.

For example, instead of writing that you simply worked on a data pipeline, describe how you built an incremental transformation process, implemented duplicate checks, or validated relationships between datasets.

This approach helps recruiters understand your practical contribution and the relevance of your experience to the position.

Work Location and Employment Details

The position is based in Gurgaon, Haryana, India, and is advertised as a full-time opportunity. The posting specifies a foundational experience range of 0–2 years, making it relevant to recent graduates and early-career professionals who meet the stated educational and technical requirements.

The job listing does not specify a confirmed salary range, detailed benefits package, or work-from-home arrangement. Candidates should consult the official recruitment team or application portal for clarification on compensation, working arrangements, and the selection process.

The company describes its broader mission around improving healthcare delivery and supporting physicians and patients through technology. Within this environment, reliable data management and reporting can contribute to informed operational and clinical decision-making.

Keywords for Resume

Associate Software Engineer • Data Engineering • SQL • Python • Java • MySQL • PostgreSQL • Snowflake • dbt • Apache Airflow • ETL • ELT • Data Modeling • Data Warehousing • Data Pipelines • Data Quality • Data Validation • Unit Testing • Sigma Computing • Tableau • Power BI • REST APIs • CI/CD • Agile Methodology • Database Design • Analytical Problem Solving

Career Takeaway

This position offers early-career engineers an opportunity to apply database, programming, and analytical skills in a professional technology environment. Candidates with strong SQL fundamentals, relevant project experience, and an understanding of data transformation and workflow orchestration should focus on demonstrating how they build reliable, maintainable data solutions. Familiarity with Snowflake, dbt, and Airflow can further strengthen an application, particularly when supported by practical examples.