{"id":1417309,"url":"https://alion.io/job/algoleap-snowflake-engineer","title":"Snowflake 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":"services","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":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"ChatGPT","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude Code","optional":false},{"name":"Copilot","optional":false},{"name":"Cursor","optional":false},{"name":"dbt","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Collibra","optional":true},{"name":"GCP","optional":true}],"status":"live","first_seen_at":"2026-09-28T17:56:10Z","employer_posted_date":null,"last_verified_at":"2026-09-28T17:56:10Z","board_verified":false,"closed_at":null,"days_open":39,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":39},"description":"Design, build, and maintain robust ETL/ELT pipelines feeding a Snowflake-based data platform\n\n Build and manage integrations using SnapLogic to connect source systems, APIs, and downstream consumers\n Develop and maintain data models and transformations in dbt, including tests, documentation, and CI/CD-based deployment\n Design dimensional and/or medallion-style (Bronze/Silver/Gold) data architectures that balance performance, cost, and usability\n Use AI-assisted tools to accelerate development — generating boilerplate code, drafting SQL/dbt models, writing documentation, debugging pipeline failures, and summarising data quality issues\n Partner with data quality, governance, and analytics teams to ensure data is well-modelled, well-documented, and trustworthy\n Optimise Snowflake warehouse performance and cost (query tuning, clustering, resource monitors)\n Write clean, tested, version-controlled code and contribute to CI/CD pipelines\n Mentor junior engineers, including on how to use AI tools responsibly and effectively (e.g., reviewing AI-generated code, not blindly trusting output)\n Contribute to internal standards for prompt patterns, reusable AI workflows, or tooling that make the whole team faster\n\nCore Skills\n\n Snowflake — strong hands-on experience with data modelling, performance tuning, security/access, and cost management\n SnapLogic — building and maintaining integration pipelines and connecting heterogeneous source systems\n dbt — writing modular, tested transformations; managing dependencies, macros, and documentation\n Data Modelling — dimensional modelling, medallion/layered architectures, normalisation vs. denormalisation trade-offs\n Strong SQL and at least one scripting language (Python preferred)\n Familiarity with orchestration tools (Airflow, ADF, or similar)\n Working knowledge of git-based CI/CD workflows\n\nAi-augmented Working Style (what We're Looking For)\n\n Regularly uses AI coding assistants (Copilot, Claude Code, Cursor, ChatGPT, etc.) as part of the daily workflow — not just for one-off snippets\n Comfortable prompting AI tools for tasks like generating dbt models, writing test cases, summarising data quality issues, or drafting documentation\n Applies good judgement about when AI output needs review vs. can be trusted — treats AI as a fast first draft, not a final answer\n Curious about applying AI to structural problems: pipeline debugging, anomaly detection, metadata generation, code review support\n Comfortable working in an environment where AI-usage practices are still evolving, and contributes ideas to shape them\n\nNICE TO HAVE\n\n Experience with data quality tooling (SODA,Collibra, or similar)\n Exposure to cloud platforms (Azure, AWS, or GCP)\n Experience in a regulated or enterprise-scale data environment\n Prior experience mentoring or leading a small pod of engineers\n\nExperience\n\n 8+ years in data engineering, with at least 4+ years focused on Snowflake and modern ELT tooling (dbt)\n\nTrack record of delivering production-grade pipelines at 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