Design, build, and maintain reliable analytics datasets, semantic models, and data products for engineering, manufacturing, testing, quality, and business use cases.

- Develop and optimize SQL- and Python-based data transformations that convert raw data

into consistent, documented, and reusable analytical models.
- Proven ability to improve data quality, model performance, reporting adoption, and

decision-making through cross-functional collaboration.
- Partner with business stakeholders, engineers, analysts, and data engineers to translate

requirements into scalable analytics solutions and clear success criteria; document business logic, data lineage, model assumptions, and solution design; and share knowledge with global teams.
- Build and enhance dashboards, reports, and self-service analytics that communicate

trends, performance, risks, and opportunities effectively.
- Create and maintain dimensional data models, metrics definitions, and data dictionaries to

establish trusted and consistent reporting.
- Implement data quality checks, validation rules, monitoring, and reconciliation processes to

improve the accuracy, completeness, and reliability of analytics data.
- Investigate data anomalies, perform root-cause analysis, and work with source-system

owners to resolve data issues.
- Improve the performance, maintainability, and usability of analytical queries, pipelines,

dashboards, and datasets.
- Use version control, code reviews, testing, and release practices to deliver governed and

supportable analytics solutions.
- Protect sensitive data and follow applicable data security, access control, and governance

practices.
- Continuously evaluate relevant technologies and analytical methods and contribute ideas for





current capabilities and process improvements. Who You Are (Basic Qualifications)
- Bachelor's or master's degree in computer science, Data Science, Engineering, Information

Systems, Statistics, or related technical field.
- 2-3 years of total experience, including at least 2 years of analytics engineering, business

intelligence, data engineering, or a closely related role.
- Working knowledge of ETL/ELT patterns and data pipeline fundamentals.
- Strong SQL skills, including complex queries, joins, aggregations, window functions, query

optimization, and the development of reusable data models.
- Practical experience using Python for data preparation, analysis, automation, and validation,

with libraries such as Pandas and NumPy.
- Experience with source control tools such as Git and work-management or bug-tracking

tools such as JIRA.
- Ability to define and validate business metrics, analyze trends, test assumptions, and

communicate findings clearly.
- Strong analytical, problem-solving, documentation, and stakeholder collaboration skills,

including the ability to explain complex technical concepts to global teams. What Will Put You Ahead
- Experience applying analytics in engineering, manufacturing, testing, or product-quality

environments.
- Experience designing dimensional models and developing curated datasets, semantic

layers, or data marts for reliable reporting and analytics.
- Experience with modern analytics engineering practices and tools, including dbt, automated

testing, data contracts, orchestration, and CI/CD.
- Knowledge of statistical analysis, predictive analytics, anomaly detection, or machine-

learning methods for generating actionable insights.

📌 Analytical Engineer (Bengaluru)
🏢 Careernet
📍 Bengaluru