{"id":3379974,"url":"https://alion.io/job/spydra-microsoft-fabric-data-engineer-data-modeling","title":"Microsoft Fabric Data Engineer - Data Modeling","company":{"id":3015206,"name":"Spydra","domain":"spydra.app","url":"https://alion.io/company/spydra","size_band":"1-10","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":19000,"max_usd":41000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":75},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Dimensional Modeling","optional":false},{"name":"Microsoft Fabric","optional":false},{"name":"pySpark","optional":false},{"name":"SQL","optional":false},{"name":"Azure","optional":true},{"name":"ETL/ELT","optional":true},{"name":"Power BI","optional":true},{"name":"Python","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-28T04:08:05Z","employer_posted_date":null,"last_verified_at":"2026-10-28T04:08:05Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"Job Description : Microsoft Fabric Data Engineer\n\nKey Skills : Data Modelling, Microsoft Fabric Notebook, Fabric Pipeline, Lakehouse, Warehouse, OneLake\n\nRole Overview : \n\nWe are looking for a skilled Microsoft Fabric Data Engineer with strong expertise in data modelling and hands-on experience with Microsoft Fabric. The ideal candidate will be responsible for designing scalable data models, developing data pipelines, and building efficient Lakehouse and Warehouse solutions to support enterprise analytics and reporting.\n\nKey Responsibilities : \n\n- Design, develop, and maintain robust data models for analytical and reporting requirements.\n\n- Build and optimize data processing solutions using Microsoft Fabric Notebooks.\n\n- Develop, orchestrate, monitor, and troubleshoot Microsoft Fabric Data Pipelines.\n\n- Design and implement Lakehouse solutions for structured and semi-structured data processing.\n\n- Develop and maintain Fabric Warehouse solutions for analytical workloads.\n\n- Apply OneLake concepts, including unified data storage, data organization, and data access across the Microsoft Fabric ecosystem.\n\n- Optimize data ingestion, transformation, query performance, and storage efficiency.\n\n- Implement data quality checks, validation rules, and data consistency measures.\n\n- Collaborate with data analysts, BI developers, architects, and business stakeholders to translate requirements into scalable data solutions.\n\n- Follow best practices for data security, governance, performance, and maintainability.\n\nMandatory Skills : \n\n- Data Modelling : Strong knowledge of conceptual, logical, and physical data modelling; dimensional modelling; star and snowflake schemas; facts and dimensions; and slowly changing dimensions (SCD).\n\n- Microsoft Fabric Notebook : Hands-on experience developing data transformation and processing solutions using notebooks, preferably with PySpark and SQL.\n\n- Fabric Pipeline : Experience building and managing data ingestion, orchestration, scheduling, and dependency workflows.\n\n- Lakehouse : Understanding of Lakehouse architecture, Delta tables, data ingestion, and transformation patterns.\n\n- Warehouse : Experience designing analytical warehouse structures and implementing efficient SQL-based data solutions.\n\n- OneLake Concepts : Understanding of OneLake architecture, data organization, shortcuts, and data sharing across Fabric workloads.\n\nPreferred Skills : \n\n- Experience with SQL and Python/PySpark.\n\n- Familiarity with medallion architecture (Bronze, Silver, Gold).\n\n- Understanding of ETL/ELT design patterns and incremental data loading.\n\n- Knowledge of data governance, security, and performance optimization.\n\n- Exposure to Power BI and enterprise analytics environments.\n\nExperience and Qualifications : \n\n- Relevant hands-on experience in data engineering and Microsoft Fabric.\n\n- Strong practical experience in data modelling and analytical data architecture.\n\n- Experience delivering production-ready data solutions in a cloud-based environment.\n\n- Microsoft Fabric or relevant Azure data certifications are an advantage.\n\nSuccess Criteria : \n\n- Deliver scalable and maintainable data models.\n\n- Build reliable and efficient Fabric pipelines and notebooks.\n\n- Implement effective Lakehouse and Warehouse solutions.\n\n- Apply OneLake capabilities to support unified data access and reuse.\n\n- Ensure data quality, performance, and reliability across data workflows.\nSkills\nData Engineering, Microsoft Fabric, PySpark, SQL, Python, ETL, DataLake, Data Warehousing, Data Modeling","description_format":"text","description_chars":3540,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Asset Tokenization"],"lifecycle":[{"event":"open","at":"2026-10-28T04:30:50Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring 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