{"id":3263778,"url":"https://alion.io/job/continental-industry-it-engineer-data-lakehouse","title":"IT Engineer - Data Lakehouse","company":{"id":92907,"name":"Continental Industry","domain":"continental-industry.com","url":"https://alion.io/company/continental-industry","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":{"grade":"B","score":75,"open_postings":65,"ghost_share":0,"stale_share":0.985,"repost_share":0,"time_to_fill_p50_days":22,"computed_at":"2026-10-29T05:49:05Z"}},"role":"Support","role_family":"Support","seniority":"middle","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":9500,"max_usd":22000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":755},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"Machine Learning","optional":false},{"name":"Master Data Management","optional":false},{"name":"Power BI","optional":false},{"name":"pySpark","optional":false},{"name":"Scala","optional":false},{"name":"Scrum","optional":false},{"name":"Python","optional":true},{"name":"Spark","optional":true}],"status":"closed","first_seen_at":"2026-10-26T04:18:07Z","employer_posted_date":"2026-10-26","last_verified_at":"2026-10-29T07:32:47Z","board_verified":false,"closed_at":"2026-10-29T07:32:47Z","days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"Continental develops pioneering technologies and services for sustainable and connected mobility of people and their goods. Founded in 1871, the technology company offers safe, efficient, intelligent, and affordable solutions for vehicles, machines, traffic and transportation. In 2023, Continental generated sales of €41.4 billion and currently employs around 200,000 people in 56 countries and markets.\nGuided by the vision of being the customer's first choice for material-driven solutions, the ContiTech group sector focuses on development competence and material expertise for products and systems made of rubber, plastics, metal, and fabrics. These can also be equipped with electronic components in order to optimize them functionally for individual services. ContiTech's industrial growth areas are primarily in the areas of energy, agriculture, construction, and surfaces. In addition, ContiTech serves the automotive and transportation industries as well as rail transport.\nThe IT Digital Enablement and Data Services Competence Center of ContiTech caters to all the Business Areas in ContiTech and responsible among other on areas of Data & Analytics, Smart Factory, Web and Mobile Software Development and AI.\nThe team for Data services specializes in all platforms, business applications and products in the domain of data and analytics, covering the entire spectrum including AI, machine learning, data science, data analysis, reporting and dashboarding.\n Key targets of the job profile\n- Design, develop, and operate scalable and maintainable data pipelines in the Azure Databricks environment\n- Develop all technical artefacts as code, implemented in professional IDEs, with full version control and CI/CD automation\n- Enable data-driven decision-making in Supply Chain Management (SCM) by ensuring high data availability, quality, and reliability\n- Implement data products and analytical assets using software engineering principles in close alignment with business domains and functional IT\n- Apply rigorous software engineering practices such as modular design, test-driven development, and artifact reuse in all implementations\nDimensions/ Organization/ Magnitude\n- Global delivery footprint; cross-functional data engineering support across SCM domains\n- Collaboration with business stakeholders, functional IT partners, product owners, architects, ML/AI engineers, and Power BI developers\n- Agile, product-team structure embedded in an enterprise-scale Azure & Azure Databricks environment\nMain Tasks:\n(1) Data Pipeline Development\nDesign scalable batch and streaming pipelines in Azure Databricks using PySpark and/or Scala\nImplement ingestion from structured and semi-structured sources (e.g., SAP, APIs, flat files)\nBuild bronze/silver/gold data layers following the defined lakehouse layering architecture & governance\n(2) Data & Analytics business applications\nImplement use-case driven dimensional models (star/snowflake schema) tailored to SCM needs\nEnsure compatibility with reporting tools (e.g., Power BI) via curated data marts and semantic models\n(3) Data Warehouse Integration\nImplement enterprise-level data warehouse models (domain-driven 3NF models) for SCM data, closely aligned with data engineers for other business domains\nDevelop and apply master data management strategies (e.g., Slowly Changing Dimensions)\n(4) Data Quality & Testing\nDevelop automated data validation tests using frameworks\nMonitor pipeline health, identify anomalies, and implement quality thresholds\nEstablish data quality transparency by defining and implementing meaningful data quality rules with source system and business stakeholders and implementing related reports\n(5) Data Engineering Excellence\nDevelop and structure pipelines using modular, reusable code in a professional IDE\nApply test-driven development (TDD) principles with automated unit, integration, and validation tests\nIntegrate tests into CI/CD pipelines to enable fail-fast deployment strategies\nCommit all artifacts to version control with peer review and CI/CD integration\n(6) Collaboration & Requirements Engineering\nWork closely with Product Owners to refine user stories and define acceptance criteria\nTranslate business requirements into data contracts and technical specifications\nParticipate in agile events such as sprint planning, reviews, and retrospectives\n(7) Documentation & Governance\nDocument pipeline logic, data contracts, and technical decisions in markdown or auto-generated docs from code\nAlign designs with governance and metadata standards (e.g., Unity Catalog)\nTrack lineage and audit trails through integrated tooling\n(8) Performance Optimization\nProfile and tune data transformation performance\nReduce job execution times and optimize cluster resource usage\nRefactor legacy pipelines or inefficient transformations to improve scalability\nRequired knowledge:\nStrong experience with modern data engineering technologies, including Azure Databricks, PySpark, Scala, Delta Lake, and Azure Data Lake Storage (ADLS Gen2).\nSolid understanding of data modeling, ETL/ELT architecture, orchestration frameworks, and scalable data platform design.\nExperience with CI/CD pipelines, DevOps practices, Git-based development workflows, and Infrastructure as Code.\nProficiency in agile software delivery methods, including Scrum, SAFe, test-driven development, and collaborative development practices.\nGood knowledge of data warehouse concepts, including dimensional modeling, normalization, star schemas, and Slowly Changing Dimensions (SCDs).\nUnderstanding of data governance, metadata management, and best practices for operating lakehouse environments.\nFamiliarity with business intelligence and reporting solutions, preferably including Power BI and tabular analysis tools.\nBasic understanding of advanced analytics, data mining, machine learning concepts, and AI-driven use cases.\nKnowledge of AI services, large language models (LLMs), and API integration concepts.\nUnderstanding of supply chain management processes, data structures, and business KPIs is beneficial.\nEducation / Certification: Degree in Computer Science, Data Engineering, Information Systems, or related discipline.\nCertifications in software development and data engineering (e.g., Databricks DE Associate, Azure Data Engineer, or relevant DevOps certifications).\nProfessional Experience: 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.\nProject and/or Process Experience: Proven experience in implementing complex data pipelines and contributing to full lifecycle data projects (development to deployment)\nExperience in at least one business domain: SCM or a comparable field\nLeadership Experience: Not required; however, experience mentoring junior developers or leading implementation workstreams is a plus\nIntercultural / International Experience: Experience working in international teams across multiple time zones and cultures, preferably with teams in India, Germany, and the Philippines.\n The well-being of our employees is important to us. That's why we offer exciting career prospects and support you in achieving a good work-life balance with additional benefits such as:\nTraining opportunities\nMobile and flexible working models\nSabbaticals\nand much more...\nSounds interesting for you? Click here to find out more.\nDiversity, 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.\nReady to drive with Continental? Take the first step and fill in the online application.\nReady to drive with Continental? 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