{"id":1992136,"url":"https://alion.io/job/stratzi-ai-data-architect","title":"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":28000,"max_usd":55000,"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 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":"Delta Lake","optional":false},{"name":"DuckDB","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},{"name":"Amazon ECS","optional":true},{"name":"dbt","optional":true},{"name":"Great Expectations","optional":true}],"status":"live","first_seen_at":"2026-10-07T06:52:15Z","employer_posted_date":null,"last_verified_at":"2026-10-07T06:52:15Z","board_verified":false,"closed_at":null,"days_open":5,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":5},"description":"We are building a data lake/lakehouse for SCADA and industrial telemetry, storing years of high-frequency time-series data from RTUs, PLCs, IEDs, and historians, along with asset registers, maintenance records, and event logs. This role will own the data storage and database platform end-to-end, from lakehouse architecture, partitioning, and schema evolution to database performance, query optimization, governance, backup, and recovery. This is a hands-on platform architecture role, not a pure design/architecture position. You should be equally comfortable designing a lakehouse using Iceberg/Delta/Hudi and tuning queries on a busy PostgreSQL/time-series database.\nThe core responsibilities for the job include the following:\nData Lake / Lakehouse Architecture:\nOwn lakehouse architecture, including storage layout, open table formats (Apache Iceberg, Delta Lake, or Hudi), catalog, and medallion/zone architecture.\nDesign partitioning, clustering, and compaction strategies for large-scale time-series data.\nSolve the small-files problem created by streaming SCADA ingestion.\nDefine file sizing, sorting, and maintenance standards.\nDesign schema evolution and versioning for tag additions, renames, and vendor changes.\nBuild rollups, downsampling, and retention tiers for raw, aggregated, and archival data.\nOwn and optimize the query layer using Trino/Presto, Spark SQL, DuckDB, or equivalent.\nDatabase Engineering:\nOwn relational and time-series databases supporting the platform.\nDesign schemas, indexes, and queries and perform query-plan-based optimization.\nManage connection/resource utilization, capacity planning, HA/replication, backup, and recovery.\nDefine upgrade and migration strategies.\nDecide what data should remain in operational/time-series databases versus the lake.\nEnsure reconciliation between the lake, operational stores, and source historians.\nGovernance and Platform Operations:\nDefine data cataloging, lineage, access control, and audit standards.\nManage storage growth, infrastructure capacity, and cloud/on-premise costs.\nSet technical standards and review architecture/designs.\nMentor data engineering teams.\nWork with OT and network teams on data movement across segmented zones, the Purdue model, DMZ, and unidirectional gateways.\nRequirements:\nWe are looking for someone hands-on, technically strong, and architecture-oriented, with deep experience in data lakes/lakehouses, databases, and high-volume time-series data.\nCandidates with exposure to SCADA, industrial/OT, railway/metro, historians, or other large-scale telemetry environments will be strongly preferred.\n5+ years in data engineering, database engineering, or data architecture.\n3+ years of hands-on production experience with data lake/lakehouse platforms.\nDeep expertise in at least one open table format: Apache Iceberg, Delta Lake, or Apache Hudi.\nStrong understanding of table maintenance, snapshots, compaction, and failure scenarios.\nStrong object storage experience: S3, ADLS, GCS, MinIO, Ceph, or equivalent.\nHands-on experience with distributed query engines such as Trino/Presto, Spark, or equivalent, including performance tuning.\nExpert SQL skills and strong RDBMS expertise (PostgreSQL, Oracle, SQL Server, or similar).\nExperience with query plans, indexing, and database performance optimization.\nExperience handling large-scale time-series data using TimescaleDB, InfluxDB, ClickHouse, industrial historians, or equivalent.\nExperience with metadata/catalog platforms such as Hive Metastore, AWS Glue, Unity Catalog, Nessie, Polaris, or similar.\nStrong Python and experience with a JVM language or equivalent platform-level programming depth.\nStrong infrastructure knowledge: Linux, containers, Kubernetes, IaC, and CI/CD.\nProven experience defining technical standards that other engineers build against.\nGood to Have:\nIndustrial/OT data experience with AVEVA/OSIsoft PI, Wonderware, GE Proficy, Siemens WinCC, Ignition, or similar historians.\nExperience with SCADA, RTUs, PLCs, IEDs, tag namespaces, and asset hierarchies.\nRailway/Metro domain experience in traction power, signalling, interlocking, tunnel ventilation, ECS/BMS, rolling stock, AFC, or depot systems.\nExperience with air-gapped or on-premises deployments.\nKnowledge of OT security, IEC 62443, and segmented network architectures.\nKnowledge of ISA-95 or equivalent asset information modelling.\nExperience with dbt, Great Expectations, Soda, OpenLineage, DataHub, or Amundsen.\nExperience working in regulated engineering environments.\nFamiliarity with railway assurance standards such as EN 50126 / EN 50128 / EN 50129.","description_format":"text","description_chars":4604,"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":"2026-10-07T06:52:15Z"}],"visa":[],"liveness":{"score":89,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.886,"p_room":1,"age_days":5,"expected_fill_days":29,"reasons":["seen:5","velocity","win:early"],"computed_at":"2026-10-12T06:55:09Z"},"pay":null,"html_url":"https://alion.io/job/stratzi-ai-data-architect","json_url":"https://alion.io/job/stratzi-ai-data-architect.json","meta":{"generated_at":"2026-10-12T07:30:34Z","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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