Data Engineer - Recommender at METRO/MAKRO

Pune, maharashtra, India -

Full Time

Start Date

Immediate

Expiry Date

16 Feb, 26

Salary

0.0

Posted On

18 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Analysis, SQL, Python, Statistical Knowledge, A/B Testing, Dashboarding Tools, Data Visualization, Collaboration, Problem Solving, Communication, Machine Learning, Data Quality, KPI Development, Version Control, Customer Orientation, Recommender Systems, Google Cloud Platform

Industry

Wholesale

Description

Company Description Metro Global Solution Center (MGSC) is internal solution partner for METRO, a €31.6 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 HR, Finance, Branding, 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 10 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: 950-1000 Headquarters: Pune, Maharashtra, India Type: Privately Held Inception: 2011 Job Description Key Responsibilities Building and maintaining dashboards tailored to the target audience to track key KPIs and in-depth reports for a heavy ML-driven product; Digging into data to uncover new use cases and identify potential areas for improvement; Leading the team’s experimentation practice; Identifying and assisting with resolving data quality issues for accurate tracking and reporting; Collaborating closely with product teams and stakeholders to enable data-driven processes; Developing new KPIs as needed; Promoting and educating product teams and (potential) stakeholders about data usage. Qualifications Education Bachelor’s or Master’s degree in Computer Science, Software Engineering, or equivalent practical experience. Work Experience & Skills 5-8 years of experience in relevant domain Strong analytical and critical thinking skills; Strong communication skills with proven experience translating complex data insights into clear, actionable recommendations for both technical and non-technical stakeholders; Advanced SQL and query optimization knowledge, preferably in BigQuery; Proven experience with visualizing data in an insightful way for the right audience with Looker Studio, Microstrategy, Tableau, Looker or other dashboarding tools; Proven experience with A/B testing (preferably server-side testing), including experiment design, technical setup and statistical evaluation of results (e.g., in Growthbook, Bayesian statistical engine); Strong statistical knowledge (e.g., data distributions, parametric and non-parametric statistical tests, type I and II errors and summary statistics, regression and tree-based models; time series analysis and Bayesian statistics would be a plus); Proficient in Python for conducting ad-hoc and exploratory data analyses, primarily using Jupyter notebooks; Solid understanding of version control with Git; Very good fluency in English (written and verbal). Strong problem-solving mindset with a process-oriented approach. Nice-to-Have Experience with cross-functional collaboration throughout the broader organization; Self-aware, positive, and strongly committed to customer orientation; Good understanding of recommender systems is a plus; Experience with Google Cloud Platform analytical technologies is a plus.

Responsibilities

The Data Engineer will build and maintain dashboards to track key KPIs and generate in-depth reports for a machine learning-driven product. They will also collaborate with product teams to enable data-driven processes and identify areas for improvement.