{"id":3418918,"url":"https://alion.io/job/algoleap-senior-snowflake-data-engineer","title":"Senior Snowflake Data Engineer","company":{"id":3800168,"name":"Algoleap","domain":"algoleap.com","url":"https://alion.io/company/algoleap-technologies","size_band":"1001-5000","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":"full_time","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":19500,"max_usd":42000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":75},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure Data Factory","optional":false},{"name":"Azure DevOps","optional":false},{"name":"Cortex","optional":false},{"name":"Embeddings","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Azure","optional":true},{"name":"Prometheus","optional":true}],"status":"live","first_seen_at":"2026-10-28T17:25:46Z","employer_posted_date":null,"last_verified_at":"2026-10-28T17:25:46Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"Job Title:Senior Snowflake Data Engineer – Data Platform & AI\nJob Location: Hyderabad\nExperience: 6–10 years\nStart Date: As soon as possible\n\nRole Overview \n\nWe are seeking a Senior Snowflake Data Engineer to design, develop, and maintain scalable data platform solutions for the Pacific Data Platform. The role covers end-to-end data ingestion, transformation, data modeling, performance optimization, security, and data quality.\n\nThe role also involves developing AI-enabled solutions using Snowflake Cortex AI, Cortex Analyst, and Cortex Searchto support natural-language analytics, intelligent search, and insights from structured and unstructured data.\n\nThe successful candidate will work closely with cross-functional teams to translate business requirements into reliable, secure, and cost-effective solutions. They should be self-directed and comfortable supporting the data needs of multiple teams, systems, and products.\n\nKey Responsibilities\n\nSnowflake Data Engineering\n\nDesign and implement scalable Snowflake data architectures for storage, processing, reporting, analytics, and AI use cases and strong sql,\nDevelop and maintain ETL/ELT pipelines using SQL, Python, Snowpark, SnapLogic, and/or Azure Data Factory.\nIntegrate structured and semi-structured data from databases, APIs, enterprise applications, and files, and prepare unstructured content for AI processing.\nImplement full-load, incremental-load, and change data capture processes with appropriate validation, reconciliation, and error handling.\nDevelop and maintain dimensional and relational data models that accurately represent business requirements.\nBuild and optimize tables, views, stored procedures, tasks, streams, and dynamic tables where appropriate.\nAnalyze query profiles and optimize warehouse sizing, clustering, partition pruning, and caching to improve performance and control costs.\nReview existing SQL and Python code to improve efficiency, security, robustness, and maintainability.\nImplement data quality checks for completeness, accuracy, consistency, validity, and duplication.\n\nSnowflake AI and Cortex\nDesign and implement AI-enabled data solutions using Snowflake Cortex AI and Snowpark,Cortex Analyst, Cortex Search and Agent creation\nApply Cortex AI functions to relevant use cases such as text summarization, classification, information extraction, and data enrichment.\nCollaborate with business stakeholders to identify practical AI use cases across property, lease, availability, client, and market data.\n\nPlatform Operations and Collaboration \nImplement Snowflake role-based access control, masking policies, row access policies, and auditing in line with enterprise security requirements.\nMonitor pipeline execution, data freshness, warehouse utilization, failures, and service performance.\nEstablish logging, alerting, retry, and recovery mechanisms for reliable production operations.\nMaintain version-controlled code and support automated deployments through Git and Azure DevOps.\nDocument architectures, data mappings, pipelines, semantic definitions, and operational procedures.\nCollaborate with application engineers, analysts, architects, and business stakeholders throughout delivery.\nMentor junior engineers and promote engineering standards, code reviews, and continuous improvement.\nKey Qualifications \n\nRequired Qualifications\nBachelor's or master's degree in Computer Science, Information Technology, Engineering, or a related field. \n 6–10 years of relevant experience in data engineering, database development, or cloud data platforms.\nStrong hands-on experience with Snowflake architecture, data loading, SQL development, performance optimization, and security.\nAdvanced SQL skills, including complex joins, window functions, stored procedures, query profiling, and troubleshooting.\nStrong Python programming skills for data processing, automation, API integration, and error handling.\nExperience with Snowpark for data transformations and application integration.\nExperience building pipelines using Azure Data Factory, or comparable integration tools.\nPractical experience with Snowflake Cortex AI, Cortex Analyst, and Cortex Search.\nAbility to develop semantic views or semantic models with clear business definitions and validated analytical queries.\nUnderstanding of natural-language-to-SQL, semantic and keyword search, embeddings, text chunking, and RAG.\nExperience integrating Snowflake AI capabilities with applications using APIs or 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