Job Overview
Role: Software Engineer – Analytical Engineering Location: Hyderabad Experience: 1–3 Years Qualification: Computer Science, Physics, Mathematics, Data Science, Pharmaceutical Science, Engineering Key Skills: SQL, Spark, Python, Data Modeling, Data Engineering, AWS, Airflow, dbt
Job Description
The Software Engineer of Analytical Engineering position at Bristol Myers Squibb (BMS) in Hyderabad focuses on transforming raw data into structured, business-ready datasets. This role bridges data engineering and data analytics, supporting the development and maintenance of core data models, centralized data layers, scalable data products, and reliable pipelines that enable business intelligence and data-driven decision-making. The engineer will also work closely with AI and ML teams to develop data foundations for AI-enabled internal tools, contributing to the delivery of trustworthy and well-structured data for various analytical use cases.
Roles and Responsibilities
- Data Transformation: Transform raw or semi-structured data into contextualized data models using an ELT framework, emphasizing dbt for SQL-based transformations.
- Data Layer Development: Help develop and maintain a centralized data layer capable of delivering data products at scale, ensuring data consistency, reuse, governance, and maintainability.
- Pipeline Optimization: Contribute to pipeline optimization and scalable data products.
- AI/ML Data Foundation: Work with AI and ML teams to develop data foundations for AI-enabled internal tools, ensuring AI-ready data.
- Business Intelligence Support: Ensure data is structured and ready for use by analysts and business intelligence teams (Tableau, Power BI, Looker).
- Data Quality: Ensure data quality and testing, promoting DRY (Don't Repeat Yourself) code principles.
- Stakeholder Communication: Gather requirements and understand business needs from stakeholders, communicating effectively.
- Documentation: Maintain technical documentation.
- Agile Development: Work within an Agile development environment using tools like JIRA, Confluence, and ServiceNow.
Skills and Eligibility Criteria
Educational Background: Academic background in Computer Science, Physics, Mathematics, Data Science, Pharmaceutical Science, or Engineering is preferred.
Experience: 1–3 years of experience in analytical engineering, data modeling, data engineering, or a similar role. Hands-on exposure to dbt is useful but not mandatory.
Mandatory Technical Skills:
- Proficiency in SQL (JOINs, CTEs, Subqueries, Window functions, Aggregations, CASE expressions, Date functions, Query optimization).
- Proficiency in Spark (DataFrames, Transformations and actions, Partitioning, Distributed processing, PySpark, Performance considerations).
- Proficiency in Python (Functions and modules, Object-oriented programming, Exception handling, File processing, Data structures, APIs, NumPy and Pandas, Testing).
- Knowledge of data engineering tools such as AWS and Airflow.
- Knowledge of visualization tools such as Tableau, Power BI, or Looker.
- Experience with Git, SVN, and Agile development.
- Experience or foundational capability in data modeling (Entities and relationships, Fact and dimension tables, Star schemas, Snowflake schemas, Primary and foreign keys, Granularity, Data quality).
Competencies:
- Analytical and problem-solving ability.
- Developing ability to gather requirements and understand business needs.
- Good communication and presentation skills.
- Familiarity with AI and machine learning, predictive modeling.
- Familiarity with JIRA, Confluence, ServiceNow, and Microsoft tools.
- Knowledge of Oracle, Amazon Redshift, PostgreSQL, CDP Impala, Amazon Athena.