Senior AI Engineer Manager

Overview

Lead Ecolab's enterprise data engineering platform across Databricks, Snowflake and Azure, owning platform operations, integration, observability, data quality, lineage, cataloging, security and engineering enablement, and leading a platform engineering team.

What you'll do

• Lead the enterprise data engineering platform operating model across Databricks, Snowflake, Azure Data Lake, ingestion, transformation, orchestration, governance, observability, monitoring, cataloging, lineage, and data quality.
• Own platform administration, including workspaces, clusters, SQL warehouses, Unity Catalog, Snowflake roles, resource monitors, workload isolation, and governance controls.
• Define observability and monitoring standards for pipelines, workflows, compute, storage, integrations, data freshness, data quality, lineage, performance, reliability, cost, SLAs, and SLOs.
• Drive automation through self-service provisioning, reusable templates, CI/CD, testing frameworks, release automation, and Infrastructure-as-Code.
• Define integration patterns across Databricks, Snowflake, Azure Data Factory, dbt, Kafka/Event Hubs, APIs, enterprise applications, databases, file sources, BI tools, and downstream platforms.
• Drive semantic and ontology layer capabilities with Data Engineering to enable consistent business definitions, reusable data products, governed consumption, and trusted analytics.
• Set ETL/ELT standards for batch, incremental, streaming, CDC, API-based, and event-driven pipelines using modern DataOps practices.
• Improve production readiness through data quality controls, lineage, metadata, cataloging, alerting, incident response, runbooks, root-cause analysis, and continuous improvement.
• Own data security and access governance, including Zero Trust, RBAC, ABAC, PBAC, data masking, encryption, secrets management, audit logging, policy enforcement, and compliance controls.
• Lead a high-performing platform engineering team through hiring, coaching, performance management, career development, workforce planning, and an inclusive engineering culture.
• Partner with Data Engineering, AI/ML Engineering, Security, Architecture, and business teams to align the platform roadmap with enterprise priorities.

What you'll need

• 10+ years of experience in data engineering, data platform engineering, cloud platform engineering, or enterprise platform operations, including 4+ years managing engineering teams.
• Hands-on experience administering Databricks and Snowflake environments, including Unity Catalog, workspace management, cluster policies, SQL warehouses, roles, resource monitors, and workload governance.
• Strong experience operating cloud-based data platforms on Microsoft Azure; AWS or Google Cloud exposure is a plus.
• Experience with CI/CD, Infrastructure-as-Code, Terraform or similar tools, platform automation, environment management, and release governance.
• Strong 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.
• Experience implementing observability, alerting, dashboards, SLA/SLO reporting, incident management, and production support for enterprise data platforms.
• Strong 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.

Nice to have

• Databricks and/or SnowPro certifications (Administrator, Data Engineer).
• Experience supporting multiple business verticals or a hub-and-spoke platform operating model.
• Exposure to LangGraph, MLflow, or other AI/ML platform tooling.
• Experience with BI, semantic layers, MLOps, LLMOps, data cataloging, lineage, observability, data quality, or monitoring tools integrated with Databricks and Snowflake.
• Experience with lakehouse architecture, medallion patterns, Delta Lake, data sharing, semantic layers, and reusable data products at enterprise scale.
• Knowledge of Spark and Snowflake performance tuning, warehouse sizing, cluster policies, auto-scaling, cost controls, and platform FinOps.
• Exposure to orchestration and workflow tools such as Azure Data Factory, Airflow, Databricks Workflows, dbt Cloud, or similar platforms.
• Experience with streaming and near-real-time patterns using Kafka, Azure Event Hubs, Spark Structured Streaming, or similar technologies.
• Familiarity with governance and cataloging platforms such as Microsoft Purview, Unity Catalog, Collibra, Alation, Atlan, OpenLineage, or similar tools.
• Experience with platform reliability practices, including SRE, health dashboards, automated alerting, incident response, capacity forecasting, resiliency testing, and disaster recovery.

Details

• Location: Bangalore; hybrid work mode.
• Reports to: Director, Data & AI Platform Engineering.