{"id":1452482,"url":"https://alion.io/job/uberlife-consulting-enterprise-data-architect","title":"Enterprise Data Architect","company":{"id":3800217,"name":"Uberlife Consulting","domain":"silverpeople.in","url":"https://alion.io/company/uberlife-consulting","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mumbai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":27000,"max_usd":54000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":14},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Airflow","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Apache Kafka","optional":false},{"name":"ClickHouse","optional":false},{"name":"ElasticSearch","optional":false},{"name":"Embeddings","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"pgvector","optional":false},{"name":"PostgreSQL","optional":false},{"name":"RAG","optional":false},{"name":"Redis","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false}],"status":"live","first_seen_at":"2026-09-29T06:29:51Z","employer_posted_date":null,"last_verified_at":"2026-09-29T06:29:51Z","board_verified":false,"closed_at":null,"days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"Role & responsibilities:\n\n- The candidate should have strong hands-on experience with PostgreSQL, ClickHouse/Redshift, Airflow, Data Warehouse, Data Lake, ETL/ELT, data modeling, and working knowledge of AI data architecture, vector databases, RAG, embeddings, AI agents, and MCP-based integrations.\n\n- The role requires the ability to design reliable OLTP data models, scalable analytical platforms, trusted enterprise data layers, and secure AI-ready data access patterns.\n\n- Design and govern enterprise data architecture across OLTP systems, Data Warehouse, Data Lake, ClickHouse, Kafka, and reporting platforms.\n\n- Own Data Warehouse and Data Lake architecture including raw, curated, trusted, data mart, semantic, and consumption layers.\n\n- Define standards for facts, dimensions, aggregates, materialized views, semantic layers, partitions, historical data, and analytical data marts.\n\n- Design OLTP data models for high-volume applications such as trading, CRM, account opening, client platforms, partner platforms, and operations.\n\n- Review transactional schema design, indexing, partitioning, archival, retention, and data access patterns across PostgreSQL and MongoDB.\n\n- Architect Kafka, CDC, ETL, and ELT pipelines for batch, near real-time, and event-driven data movement.\n\n- Ensure integration between PostgreSQL, MongoDB, Redis, Elasticsearch, Kafka, ClickHouse, Data Warehouse, and Data Lake platforms.\n\n- Define AI-ready data architecture for RAG, semantic search, embeddings, vector stores, enterprise knowledge access, and AI agent consumption.\n\n- Guide architecture for vector databases / vector search using platforms such as PostgreSQL pgvector, MongoDB Vector Search, Elasticsearch vector search, or similar tools.\n\n- Design secure MCP-based integration patterns to expose enterprise data, APIs, metadata, documents, and tools to AI assistants and agentic workflows.\n\n- Own data quality, reconciliation, metadata, lineage, data freshness, and source-to-target control frameworks.\n\n- Optimize analytical workloads across ClickHouse, warehouse queries, pipelines, dashboards, reporting layers, and AI retrieval workloads.\n\n- Support OLTP performance engineering across PostgreSQL, MongoDB, Redis, Elasticsearch, and high-concurrency application workloads.\n\n- Define security, access control, masking, audit logging, retention, compliance, HA, DR, backup, restore, observability, and capacity planning standards.\n\n- Drive modernization from legacy databases, fragmented reporting systems, and siloed data marts to a scalable enterprise data and AI-ready platform.\nSkills\nPostgreSQL, ClickHouse, ETL, Data Modeling, MongoDB, Data Warehousing, DataLake, Apache Airflow, Data Architect","description_format":"text","description_chars":2703,"description_truncated":false,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-09-29T08:00:48Z"}],"liveness":{"score":54,"band":"ok","label":"Likely open","p_open":1,"p_active":0.542,"p_room":1,"age_days":0,"expected_fill_days":30,"reasons":["seen:0","agency","velocity","win:early"],"computed_at":"2026-09-30T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/uberlife-consulting-enterprise-data-architect","json_url":"https://alion.io/job/uberlife-consulting-enterprise-data-architect.json","meta":{"generated_at":"2026-10-01T00:12:02Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"assistant","counted_by":"address","units_charged":1,"used_today":17,"day_limit":2000,"remaining_today":1983,"minute_limit":60,"resets_at":"2026-10-02T00:00:00Z"}}}