IT engineer data lakehouse

Continental; Bengaluru; Hybrid; INR 800,000 - 1,200,000; Full time

Benefits offered by this job

Training opportunities

Mobile and flexible working models

Sabbaticals

Job summary

Continental is seeking a Data Engineer to design and develop scalable data pipelines in Azure Databricks. The successful candidate will collaborate with stakeholders to enhance data quality and availability in Supply Chain Management, build robust data products, and participate in agile processes.

The role requires a degree in Computer Science and significant experience in data engineering, with a focus on delivering production-grade solutions. Flexible working models and training opportunities are offered.

Qualifications

• 3–6 years of experience in data engineering roles in enterprise environments.
• Experience implementing complex data pipelines from development to deployment.
• Familiarity with SCM or a comparable business domain.

Responsibilities

• Design and develop scalable data pipelines in Azure Databricks.
• Collaborate with various stakeholders to enable data-driven decision-making.
• Monitor pipeline health and implement quality thresholds.

Skills

Data pipeline development

Azure Databricks

PySpark

Software engineering principles

Test-driven development

Master data management

Education

Degree in Computer Science or related discipline

Certifications in data engineering

Tools

Power BI

CI/CD tools

Job description

Job Description

• Design, develop, and operate scalable and maintainable data pipelines in the Azure Databricks environment
• Develop all technical artefacts as code, implemented in professional IDEs, with full version control and CI/CD automation
• Enable data-driven decision-making in Supply Chain Management (SCM) by ensuring high data availability, quality, and reliability
• Implement data products and analytical assets using software engineering principles in close alignment with business domains and functional IT
• Apply rigorous software engineering practices such as modular design, test-driven development, and artifact reuse in all implementations
• Global delivery footprint; cross-functional data engineering support across SCM domains
• Collaboration with business stakeholders, functional IT partners, product owners, architects, ML/AI engineers, and Power BI developers
• Agile, product‑team structure embedded in an enterprise‑scale Azure environment

Main Tasks

• Design scalable batch and streaming pipelines in Azure Databricks using PySpark and/or Scala
• Implement ingestion from structured and semi‑structured sources (e.g., SAP, APIs, flat files)
• Build bronze/silver/gold data layers following the defined lakehouse layering architecture & governance
• Implement use‑case driven dimensional models (star/snowflake schema) tailored to SCM needs
• Ensure compatibility with reporting tools (e.g., Power BI) via curated data marts and semantic models
• Implement enterprise‑level data warehouse models (domain‑driven 3NF models) for SCM data, closely aligned with data engineers for other business domains
• Develop and apply master data management strategies (e.g., Slowly Changing Dimensions)
• Develop automated data validation tests using frameworks
• Monitor pipeline health, identify anomalies, and implement quality thresholds
• Establish data quality transparency by defining and implementing meaningful data quality rules with source system and business stakeholders and implementing related reports
• Develop and structure pipelines using modular, reusable code in a professional IDE
• Apply test‑driven development (TDD) principles with automated unit, integration, and validation tests
• Integrate tests into CI/CD pipelines to enable fail‑fast deployment strategies
• Commit all artifacts to version control with peer review and CI/CD integration
• Work closely with Product Owners to refine user stories and define acceptance criteria
• Translate business requirements into data contracts and technical specifications
• Participate in agile events such as sprint planning, reviews, and retrospectives
• Document pipeline logic, data contracts, and technical decisions in markdown or auto‑generated docs from code
• Align designs with governance and metadata standards (e.g., Unity Catalog)
• Track lineage and audit trails through integrated tooling
• Profile and tune data transformation performance
• Reduce job execution times and optimize cluster resource usage
• Refactor legacy pipelines or inefficient transformations to improve scalability

Additional Information

We offer exciting career prospects and support you in achieving a good work‑life balance with additional benefits such as:

• Training opportunities
• Mobile and flexible working models
• Sabbaticals

Qualifications

• Degree in Computer Science, Data Engineering, Information Systems, or related discipline.
• Certifications in software development and data engineering (e.g., Databricks DE Associate, Azure Data Engineer, or relevant DevOps certifications).
• 3–6 years of hands‑on experience in data engineering roles in enterprise environments. Demonstrated experience building production‑grade codebases in IDEs, with test coverage and version control.
• Proven experience in implementing complex data pipelines and contributing to full lifecycle data projects (development to deployment).
• Experience in at least one business domain: SCM or a comparable field.
• Not required; however, experience mentoring junior developers or leading implementation workstreams is a plus.
• Experience working in international teams across multiple time zones and cultures, preferably with teams in India, Germany, and the Philippines.

Diversity, Inclusion & Belonging are important to us and make our company strong and successful. We offer equal opportunities to everyone – regardless of age, gender, nationality, cultural background, disability, religion, ideology or sexual orientation.