{"id":1518352,"url":"https://alion.io/job/stratzi-ai-senior-data-architect","title":"Senior Data Architect","company":{"id":4474,"name":"Stratzi.ai","domain":"stratzi.ai","url":"https://alion.io/company/stratzi-ai","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":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":26000,"max_usd":52000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":13},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon ECS","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache HTTP Server","optional":false},{"name":"Apache Hudi","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"AWS Glue","optional":false},{"name":"CI/CD","optional":false},{"name":"ClickHouse","optional":false},{"name":"dbt","optional":false},{"name":"Delta Lake","optional":false},{"name":"DuckDB","optional":false},{"name":"Great Expectations","optional":false},{"name":"InfluxDB","optional":false},{"name":"Kubernetes","optional":false},{"name":"Linux","optional":false},{"name":"MinIO","optional":false},{"name":"Oracle","optional":false},{"name":"Polaris","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Presto","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"TimescaleDB","optional":false},{"name":"Trino","optional":false}],"status":"live","first_seen_at":"2026-09-30T10:36:49Z","employer_posted_date":null,"last_verified_at":"2026-09-30T10:36:49Z","board_verified":false,"closed_at":null,"days_open":12,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":12},"description":"The core responsibilities for the job include the following:\nData lake architecture:\nOwn the lakehouse design: storage layout, open table format (Iceberg, Delta Lake, or Hudi), catalog, and the medallion/zone model from raw ingest through to curated, query-ready datasets.\nDefine partitioning, clustering, and compaction strategies tuned for time-series access patterns, long time-range scans over narrow tag selections, and recent-window queries.\nSolve the small-files problem that streaming SCADA ingestion inevitably creates, and set file sizing, sort order, and maintenance job standards.\nDesign schema evolution and versioning so tag additions, renames, and vendor upgrades don't break downstream consumers.\nBuild rollup, downsampling, and retention tiers: raw at full fidelity, aggregates for dashboards, archival for regulatory, and RAMS analysis.\nChoose and own the query layer (Trino/Presto, Spark SQL, DuckDB, or warehouse engine) and tune it for the workloads that actually matter.\nDatabase engineering:\nOwn the relational and time-series databases behind the platform schema design, indexing, query tuning, connection, and resource management.\nCapacity planning, HA/replication, backup, and tested restore procedures, upgrade paths.\nDefine how the lake and the operational stores coexist: what stays hot in a time-series DB, what lands in the lake, and how the two reconcile against source historians.\nGovernance and operations:\nData cataloguing, lineage, access control, and audit, including how OT-sourced data is classified and who can reach it.\nCost and storage growth management [cloud spend / on-prem capacity, as applicable].\nSet standards, review designs, and mentor the data engineering team.\nWork with OT and network teams on data movement across segmented zones (Purdue model, DMZ, unidirectional gateways) without weakening the security boundary.\nRequirements:\n5+ years in data engineering, database engineering, or data architecture, with 3+ years specifically on data lake or lakehouse platforms in production.\nDeep expertise in at least one open table format (Apache Iceberg, Delta Lake, or Apache Hudi), including maintenance operations, snapshot management, and failure modes.\nStrong object storage fundamentals (S3, ADLS, GCS, or on-prem MinIO/Ceph layout, consistency, lifecycle policies, cost, or capacity implications).\nDistributed query engines: Trino/Presto, Spark, or equivalent, with real tuning experience.\nExpert SQL and strong RDBMS administration (PostgreSQL, Oracle, SQL Server, or similar): you can read a query plan and fix what's wrong.\nTime-series data at scale: TimescaleDB, InfluxDB, ClickHouse, or a historian, and a clear view of where each fits.\nMetadata and catalog systems (Hive Metastore, AWS Glue, Unity Catalog, Nessie, Polaris, or similar).\nPython and a JVM language, or equivalent depth for platform-level work.\nInfrastructure literacy: Linux, containers, Kubernetes, IaC, CI/CD.\nTrack record of setting technical standards other engineers build on.\nStrongly preferred:\nIndustrial or OT data experience historians (AVEVA/OSIsoft PI, Wonderware, GE Proficy, Siemens WinCC, Ignition), tag namespaces, and asset hierarchies.\nRailway or metro domain experience in traction power, signalling and interlocking, tunnel ventilation, ECS/BMS, rolling stock condition monitoring, AFC, and depot systems.\nAir-gapped or on-premise deployments and the constraints that come with them, OT security standards (IEC 62443), and segmented network architectures.\nISA-95 or equivalent asset information modelling, dbt, data quality frameworks (Great Expectations, Soda), and lineage tooling (OpenLineage, DataHub, Amundsen).\nRail assurance standards (EN 50126/50128/50129) or other regulated engineering environments.","description_format":"text","description_chars":3755,"description_truncated":false,"requirements":{"experience_years_min":5,"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":["Predictive Analytics","AI Consulting & Integration"],"lifecycle":[{"event":"open","at":"2026-09-30T10:36:49Z"}],"visa":[],"liveness":{"score":75,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.829,"p_room":0.9,"age_days":11,"expected_fill_days":29,"reasons":["seen:11","velocity","win:mid"],"computed_at":"2026-10-12T06:55:09Z"},"pay":null,"html_url":"https://alion.io/job/stratzi-ai-senior-data-architect","json_url":"https://alion.io/job/stratzi-ai-senior-data-architect.json","meta":{"generated_at":"2026-10-12T15:32:07Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per 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