Job Description

Metro Global Solution Center (MGSC) is internal solution partner for METRO, a €31.5 Billion international wholesaler with operations in 32 countries through 625 stores & a team of 85,000 people globally. Metro operates in a further 10 countries with its Food Service Distribution (FSD) business and it is thus active in a total of 34 countries.

MGSC, location wise is present in Pune (India), Düsseldorf (Germany) and Szczecin (Poland). We provide Finance, HR, IT & Business operations support to 31 countries, speak 24+ languages and process over 18,000 transactions a day. We are setting tomorrow’s standards for customer focus, digital solutions, and sustainable business models. For over 12 years, we have been providing services and solutions from our two locations in Pune and Szczecin. This has allowed us to gain extensive experience in how we can best serve our internal customers with high quality and passion. We believe that we can add value, drive efficiency, and satisfy our customers.

Website: https://www.metro-gsc.in

Company Size: 1100-1200

Headquarters: Pune, Maharashtra, India

Type: Privately Held

Inception: 2011

About the Role

We are looking for a pragmatic and curious Analytics Engineer to sit at the intersection of data engineering and data analysis. In this role, you will be the bridge between our raw data and the business stakeholders who need it.

You won’t just be building pipelines; you will be responsible for defining how the business looks at data. You will bring data engineering best practices to our analytics stack and ensure that our data models are clean, reliable, and performant.

Key Responsibilities

• Data Modeling & Transformation: Build and maintain high-quality data models using dbt and SQL Transform raw data into clear, accessible datasets (Data Marts) that power our analytics.
• The Business/Tech Bridge: Act as a translator. collaborate with Data Analyst who works with Product, Marketing, Finance teams to understand their business logic, and turn vague business requirements into concrete technical implementations.
• Tooling & Automation: Use Python to automate repetitive tasks, build custom connectors, or enhance orchestration workflows.
• BI Enablement: Optimize data specifically for Tableau Ensure that the data structure supports performant dashboards and intuitive exploration for end-users.
• Quality Assurance: Implement testing frameworks (within dbt) and CI/CD pipelines to ensure data accuracy. You will be the gatekeeper of data quality.
• Documentation: Maintain a data dictionary and documentation to ensure stakeholders understand the metrics they are looking at.

Qualifications

Hard Skills & Qualifications

Need min. 3+ year of hands-on experience.

To be successful in this role, you need a strong technical foundation:

• SQL Mastery: You write advanced, efficient, and readable SQL. You understand window functions, complex joins, and query optimization.
• DBT Experience: Proven experience using dbt (data build tool) to deploy data models. You understand snapshots, incremental models, and jinja templating.
• Python Proficiency: Ability to write clean Python code for data manipulation or orchestration tasks.
• BI Tool Knowledge (Tableau): You don’t need to be a dashboard wizard, but you must understand how Tableau consumes data (extracts vs. live, data blending) to model the data effectively upstream.
• Data Warehousing: Experience with modern cloud warehouses (e.g., Snowflake, BigQuery, or Redshift).
• Version Control: Comfortable using Git for collaboration and code versioning.

Soft Skills & Mindset

We are looking for someone who possesses the following behavioral traits:

• Business Acumen: You don't just build what is asked; you ask why You understand the underlying business mechanics (revenue drivers, churn, user lifecycles) and design your data models to reflect reality.
• Communication: You can explain complex technical constraints to non-technical stakeholders and explain business logic to engineers.
• Empathy for the End-User: You build tables with the final analyst in mind. You care about column naming conventions and usability.
• Curiosity & Problem Solving: You love digging into a data discrepancy to find the root cause, rather than applying a band-aid fix.
• Autonomy: You can take a vague requirement ("We need to understand customer retention") and drive it to a deployed solution with minimal hand-holding.