{"id":3563105,"url":"https://alion.io/job/prudent-technology-consulting-senior-data-engineer","title":"Senior Data Engineer","company":{"id":3801510,"name":"Prudent Technology & Consulting","domain":"prudentconsulting.com","url":"https://alion.io/company/prudent-technology-and-consulting","size_band":"201-500","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Keka","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-11-03T05:54:40Z"}},"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":43000,"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":"AI Agents","optional":false},{"name":"ChatGPT","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Claude Code","optional":false},{"name":"Copilot","optional":false},{"name":"Cortex","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},{"name":"Prometheus","optional":true}],"status":"live","first_seen_at":"2026-07-14T08:40:24Z","employer_posted_date":"2026-07-14","last_verified_at":"2026-11-03T08:03:53Z","board_verified":true,"closed_at":null,"days_open":112,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":111},"description":"ABOUT THE ROLE\nData Engineering team is looking for a Senior Data Engineer who combines strong data engineering fundamentals with a genuine curiosity for using AI tools to work smarter, not just harder. You will design and build scalable data pipelines and models on Snowflake, orchestrate integrations through SnapLogic, and use dbt to bring rigor and testability to our transformation layer - while also actively exploring how AI copilots and agentic tools can speed up development, debugging, documentation, and data quality work.\nThis is not an “AI engineer” role - it is a data engineering role for someone who is already fluent with tools like Cortex, GitHub Copilot, Claude, or ChatGPT in their daily workflow and sees them as a natural extension of good engineering practice, not a gimmick.\nWHAT YOU'LL DO\nDesign, build, and maintain robust ETL/ELT pipelines feeding a Snowflake-based data platform\nBuild and manage integrations using SnapLogic to connect source systems, APIs, and downstream consumers\nDevelop and maintain data models and transformations in dbt, including tests, documentation, and CI/CD-based deployment\nDesign dimensional and/or medallion-style (Bronze/Silver/Gold) data architectures that balance performance, cost, and usability\nUse AI-assisted tools to accelerate development - generating boilerplate code, drafting SQL/dbt models, writing documentation, debugging pipeline failures, and summarising data quality issues\nPartner with data quality, governance, and analytics teams to ensure data is well-modelled, well-documented, and trustworthy\nOptimise Snowflake warehouse performance and cost (query tuning, clustering, resource monitors)\nWrite clean, tested, version-controlled code and contribute to CI/CD pipelines\nMentor junior engineers, including on how to use AI tools responsibly and effectively (e.g., reviewing AI-generated code, not blindly trusting output)\nContribute to internal standards for prompt patterns, reusable AI workflows, or tooling that make the whole team faster\nCORE SKILLS\nSnowflake - strong hands-on experience with data modelling, performance tuning, security/access, and cost management\nSnapLogic - building and maintaining integration pipelines and connecting heterogeneous source systems\ndbt - writing modular, tested transformations; managing dependencies, macros, and documentation\n Data Modelling - dimensional modelling, medallion/layered architectures, normalisation vs. denormalisation trade-offs\nStrong SQL and at least one scripting language (Python preferred)\nFamiliarity with orchestration tools (Airflow, ADF, or similar)\nWorking knowledge of git-based CI/CD workflows\nAI-AUGMENTED WORKING STYLE (WHAT WE'RE LOOKING FOR)\nRegularly uses AI coding assistants (Copilot, Claude Code, Cursor, ChatGPT, etc.) as part of the daily workflow - not just for one-off snippets\nComfortable prompting AI tools for tasks like generating dbt models, writing test cases, summarising data quality issues, or drafting documentation\nApplies good judgement about when AI output needs review vs. can be trusted - treats AI as a fast first draft, not a final answer\nCurious about applying AI to structural problems: pipeline debugging, anomaly detection, metadata generation, code review support\nComfortable working in an environment where AI-usage practices are still evolving, and contributes ideas to shape them\nNICE TO HAVE\nExperience with data quality tooling (SODA, Collibra, or similar)\nExposure to cloud platforms (Azure, AWS, or GCP)\nExperience in a regulated or enterprise-scale data environment\nPrior experience mentoring or leading a small pod of engineers\nEXPERIENCE\n8+ years in data engineering, with at least 4+ years focused on Snowflake and modern ELT tooling (dbt)\nTrack record of delivering production-grade pipelines at 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