Data Engineer

Company: Continental

Website: Visit Website

Business Type: Enterprise

Company Type: Product

Business Model: B2B

Funding Stage: Series C

Industry: Retail

Salary Range: 15-40 Lacs PA

Job Description

This is the permanent role with a valued client of VidPro.

Mandatory Skills : Enterprise data, Analytics warehouse, lakehouse modeling, ML integration, Data warehouse, Data Lakehouse.

Key Responsibilities

Certifications in Databricks or Microsoft preferred.

• Govern the enterprise-wide standards for data & analytics modeling and performance within the Databricks Lakehouse.
• Drive consistency and reuse of core data & analytics artifacts and ensure scalable integration across all business domains.
• Provide expert consulting, quality assurance, and enablement for data engineering and data science teams.
• Act as a design authority for data warehouse, semantic modeling, and advanced analytics integration.
• Acts as the senior engineering point of contact for the lakehouse layer across global teams.
• Coordinates with 25+ data engineering and data science professionals across domains and geographies.
• Collaborates closely with platform architects, data scientists, governance teams, and functional IT globally.
  
  

Main Tasks

• Define enterprise 3NF and warehouse modeling standards.
• Maintain and review enterprise-wide data & analytics models and shared artifacts.
• Align naming conventions and metadata handling with governance standards.
• Guide partitioning, indexing, and performance tuning.
• Enable, steer and optimize semantic integration with Power BI, live tabular exploration and other tools.
• Own common functions, e.g. FX conversion, BOM logic, time-slicing.
• Review and approve core components for quality and reusability.
• Provide support on high-performance or high-complexity challenges.
• Align lakehouse implementation with architectural decisions.
• Collaborate with data science and AI teams on model deployment.
• Ensure seamless integration of ML/AI pipelines into the lakehouse.
• Support LLM and external API integration patterns.
• Build and maintain shared libraries and data engineering templates.
• Coach junior engineers and define TDD and as-code standards.
• Drive engineering excellence across the community of practice.
• Maintain architectural blueprints, templates, and best practices.
• Publish design guidelines and coding standards.
• Create re-usable architecture patterns for lakehouse environments.
• Monitor usage and implement auto-scaling policies.
• Analyze and optimize cluster configurations for cost-efficiency.
• Provide cost transparency and usage reporting to stakeholders.
  
  

Required Qualifications

• Degree in Computer Science or related field; certifications in Databricks or Microsoft preferred.
• 6–10 years in data engineering with focus on enterprise data & analytics warehouse, lakehouse modeling, and ML integration.
• Hands-on experience designing large-scale semantic, warehouse, and advanced analytics layers.
• Track record of architectural ownership and peer enablement with diverse teams.
• Experience working in international teams across multiple time zones and cultures, preferably with teams in India, Germany, and the Philippines.