IT Engineer, Data Lakehouse

Continental

Data Engineer designing, developing, and operating data pipelines in Azure Databricks for supply chain management. Collaborating with cross-functional teams and applying software engineering principals in a global setting.

Posted 7/18/2026full-timeRemote • 🇮🇳 IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and developing scalable data pipelines in Azure Databricks, applying rigorous software engineering practices, and ensuring high data quality and reliability for Supply Chain Management. Proficient in implementing CI/CD automation and collaborating with cross-functional teams to drive data-driven decision-making.

Highest-signal resume keywords

Azure Databricks DevelopmentData Pipeline ImplementationTest-Driven Development (TDD)CI/CD AutomationSupply Chain Management (SCM) Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringPySparkScalaModular DesignData Validation TestingDimensional ModelingData Warehouse ModelingMaster Data ManagementData Quality RulesPerformance Tuning

Soft Skills

CollaborationAgile MethodologiesCommunication

Tools & Technologies

Power BIVersion Control SystemsProfessional IDEsAPIsSAP

Certifications & Qualifications

Databricks DE AssociateAzure Data EngineerDevOps Certifications

Industry Keywords

Data PipelinesData QualityData GovernanceEnterprise EnvironmentsCross-Functional Teams

Tech Stack

Tools & technologies

AzurePySparkScalaUnity

About the role

Key responsibilities & impact

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
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

Requirements

What you’ll need

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.

Benefits

Comp & perks

Training opportunities
Mobile and flexible working models
Sabbaticals and much more