{"id":4234099,"url":"https://alion.io/job/techdome-data-engineer-3","title":"Data Engineer","company":{"id":4431620,"name":"Techdome","domain":"techdome.io","url":"https://alion.io/company/tech-dome","size_band":"51-200","is_staffing_agency":false,"employer_type":"staffing","is_intermediary":false,"listed_via":null,"ats_vendor":"Keka","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":null,"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":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Git","optional":false},{"name":"LLM","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Unstructured.io","optional":false},{"name":"Amazon Neptune","optional":true},{"name":"Embeddings","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"OCR","optional":true},{"name":"pgvector","optional":true},{"name":"Pinecone","optional":true},{"name":"PostgreSQL","optional":true}],"status":"live","first_seen_at":"2026-11-13T10:24:15Z","employer_posted_date":"2026-11-13","last_verified_at":"2026-11-14T05:32:57Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Why this role matters\nEvery AI agent, scorecard, and dashboard we deliver is only as good as the data underneath it. As a Data Engineer, you’ll build that foundation: the pipelines, connectors, indexes, vector stores, and data marts that make our AI products accurate, grounded, and auditable.\nWhat you’ll do\nDesign and build ingestion pipelines for structured data, unstructured content such as documents and PDFs, and metadata\nBuild reusable connectors to enterprise systems, catalogs, content repositories, and licensed third-party sources via APIs\nModel and build raw-to-mart data layers serving both analytics and AI use cases\nImplement metadata extraction, enrichment, and search indexing, including semantic and vector indexes for RAG\nSet up and manage the vector databases and knowledge repositories that power LLM agents\nBuild data quality rules, profiling, scoring outputs, and exception handling\nRegister lineage and maintain source registries, version tracking, and refresh controls\nDesign data stores for signals, findings, audit trails, and user feedback loops\nApply security and governance controls, including RBAC, PII and sensitivity flagging, and approved data handling\nSupport SIT and UAT data validation, defect fixes, and production releases\nWhat you bring\nStrong Python and SQL, with solid dimensional and normalized data modeling\nHands-on experience with a modern data platform such as Databricks, Snowflake, or Azure/AWS data services\nPipeline orchestration and transformation with Spark, Airflow, ADF, dbt, or similar\nREST API integration, JSON handling, and incremental or CDC ingestion patterns\nData quality frameworks and pipeline testing practices\nGit and CI/CD for data workloads\nBonus points for\nRAG data preparation: chunking, embeddings, and vector databases (Azure AI Search, pgvector, Pinecone, or similar)\nUnstructured content processing, including text extraction, OCR, and document parsing\nMetadata management, data catalogs, ontologies, or knowledge graphs such as Neptune\nExperience supporting LLM or agentic applications with grounded, traceable 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