{"id":2350352,"url":"https://alion.io/job/ecolab-senior-ai-engineer-manager","title":"Senior AI Engineer Manager","company":{"id":1754712,"name":"Ecolab","domain":"ecolab.com","url":"https://alion.io/company/ecolab-com","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":81,"open_postings":127,"ghost_share":0,"stale_share":0.945,"repost_share":0,"time_to_fill_p50_days":15,"computed_at":"2026-10-12T06:55:09Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":27000,"max_usd":55000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":27},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Zero Trust","optional":false},{"name":"Alation","optional":true},{"name":"Collibra","optional":true},{"name":"Delta Lake","optional":true},{"name":"FinOps","optional":true},{"name":"Incident Management","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"LLMOps","optional":true},{"name":"MLFlow","optional":true},{"name":"SLI/SLO/SLA","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-10-12T13:41:19Z","employer_posted_date":"2026-10-12","last_verified_at":"2026-10-12T19:14:39Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"SENIOR MANAGER, DATA ENGINEERING PLATFORM\nEcolab | Data & AI Platform Engineering\nLocation: [Bangalore/ Hybrid ]\nReports To: Director, Data & AI Platform Engineering\nTeam: Databricks AI & Data Platform / Snowflake Modern Data Platform\nROLE SUMMARY\nEcolab is seeking a Senior Manager, Data Engineering Platform to lead enterprise data platform capabilities that enable analytics, AI, and business decision-making. This role owns platform operations, integration, observability, monitoring, data quality, lineage, cataloging, security, and engineering enablement across Databricks, Snowflake, and Azure-based data platforms. The leader will deliver a secure, scalable, reliable, and cost-efficient foundation for ingestion, ETL/ELT, orchestration, governance, semantic and ontology capabilities, and production support across enterprise domains.\nKEY RESPONSIBILITIES\nLead the enterprise data engineering platform operating model across Databricks, Snowflake, Azure Data Lake, ingestion, transformation, orchestration, governance, observability, monitoring, cataloging, lineage, and data quality.\nOwn platform administration, including workspaces, clusters, SQL warehouses, Unity Catalog, Snowflake roles, resource monitors, workload isolation, and governance controls.\nDefine observability and monitoring standards for pipelines, workflows, compute, storage, integrations, data freshness, data quality, lineage, performance, reliability, cost, SLAs, and SLOs.\nDrive automation through self-service provisioning, reusable templates, CI/CD, testing frameworks, release automation, and Infrastructure-as-Code.\nDefine integration patterns across Databricks, Snowflake, Azure Data Factory, dbt, Kafka/Event Hubs, APIs, enterprise applications, databases, file sources, BI tools, and downstream platforms.\nDrive semantic and ontology layer capabilities with Data Engineering to enable consistent business definitions, reusable data products, governed consumption, and trusted analytics.\nSet ETL/ELT standards for batch, incremental, streaming, CDC, API-based, and event-driven pipelines using modern DataOps practices.\nImprove production readiness through data quality controls, lineage, metadata, cataloging, alerting, incident response, runbooks, root-cause analysis, and continuous improvement.\nOwn data security and access governance, including Zero Trust, RBAC, ABAC, PBAC, data masking, encryption, secrets management, audit logging, policy enforcement, and compliance controls.\nLead a high-performing platform engineering team through hiring, coaching, performance management, career development, workforce planning, and an inclusive engineering culture.\nPartner with Data Engineering, AI/ML Engineering, Security, Architecture, and business teams to align the platform roadmap with enterprise priorities.\nREQUIRED QUALIFICATIONS\n10+ years of experience in data engineering, data platform engineering, cloud platform engineering, or enterprise platform operations, including 4+ years managing engineering teams.\nHands-on experience administering Databricks and Snowflake environments, including Unity Catalog, workspace management, cluster policies, SQL warehouses, roles, resource monitors, and workload governance.\nStrong experience operating cloud-based data platforms on Microsoft Azure; AWS or Google Cloud exposure is a plus.\nExperience with CI/CD, Infrastructure-as-Code, Terraform or similar tools, platform automation, environment management, and release governance.\nStrong understanding of integration, ETL/ELT, CDC, streaming, orchestration, metadata, cataloging, lineage, data quality, monitoring, and DataOps practices using tools such as Azure Data Factory, Airflow, dbt, Kafka/Event Hubs, or similar platforms.\nExperience implementing observability, alerting, dashboards, SLA/SLO reporting, incident management, and production support for enterprise data platforms.\nStrong understanding of data security and access governance, including Zero Trust, RBAC, ABAC, PBAC, identity management, audit logging, encryption, data masking, secrets management, and data protection.\nPREFERRED QUALIFICATIONS\nDatabricks and/or SnowPro certifications (Administrator, Data Engineer).\nExperience supporting multiple business verticals or a hub-and-spoke platform operating model.\nExposure to LangGraph, MLflow, or other AI/ML platform tooling.\nExperience with BI, semantic layers, MLOps, LLMOps, data cataloging, lineage, observability, data quality, or monitoring tools integrated with Databricks and Snowflake.\nExperience with lakehouse architecture, medallion patterns, Delta Lake, data sharing, semantic layers, and reusable data products at enterprise scale.\nKnowledge of Spark and Snowflake performance tuning, warehouse sizing, cluster policies, auto-scaling, cost controls, and platform FinOps.\nExposure to orchestration and workflow tools such as Azure Data Factory, Airflow, Databricks Workflows, dbt Cloud, or similar platforms.\nExperience with streaming and near-real-time patterns using Kafka, Azure Event Hubs, Spark Structured Streaming, or similar technologies.\nFamiliarity with governance and cataloging platforms such as Microsoft Purview, Unity Catalog, Collibra, Alation, Atlan, OpenLineage, or similar tools.\nExperience with platform reliability practices, including SRE, health dashboards, automated alerting, incident response, capacity forecasting, resiliency testing, and disaster 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