{"id":3146570,"url":"https://alion.io/job/random-trees-lead-snowflake-data-engineer","title":"Lead Snowflake Data Engineer","company":{"id":3801016,"name":"Random Trees","domain":"randomtrees.com","url":"https://alion.io/company/random-trees","size_band":null,"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":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":29000,"max_usd":62000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":20},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"dbt","optional":false},{"name":"ETL/ELT","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-23T10:13:25Z","employer_posted_date":null,"last_verified_at":"2026-10-23T10:13:25Z","board_verified":false,"closed_at":null,"days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"Role Overview : \n\nWe are looking for a Lead Snowflake Data Engineer to lead the design, development, and optimization of enterprise-scale data pipelines and cloud data warehouse solutions. The role requires deep expertise in Snowflake, dbt, SQL, and data engineering, along with the ability to drive technical decisions, establish best practices, and guide engineering teams.\n\nResponsibilities : \n\n- Lead the design and implementation of scalable, reliable, and high-performance data pipelines using Snowflake.\n\n- Define data engineering standards, architecture, and best practices for ETL/ELT development and data transformation.\n\n- Oversee dbt implementations, including reusable data models, transformation logic, testing, and maintainability.\n\n- Drive data modeling, warehouse design, and SQL performance optimization.\n\n- Design and manage workflow orchestration using Apache Airflow.\n\n- Guide the development of data solutions using Python and/or PySpark.\n\n- Identify and resolve complex data pipeline, integration, and performance issues.\n\n- Ensure data quality, reliability, scalability, and efficient warehouse resource utilization.\n\n- Collaborate with stakeholders to translate business requirements into technical solutions.\n\n- Mentor engineers, review code and data models, and drive continuous improvement in engineering practices.\n\n- Take ownership of technical delivery, implementation decisions, and overall solution quality.\n\nRequirements : \n\n- 10 - 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