Senior AI Engineer Manager
- Compensation
- Not disclosed
- Location
- IND - Karnataka - Bangalore - EDC
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 4+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- 10+ years of experience in data engineering, data platform engineering, cloud platform engineering, or enterprise platform operations, including 4+ years managing engineering teams.
Nice to have
- Databricks and/or SnowPro certifications (Administrator, Data Engineer).
Full description
SENIOR MANAGER, DATA ENGINEERING PLATFORMEcolab | Data & AI Platform EngineeringLocation: [Bangalore/ Hybrid ]
Reports To: Director, Data & AI Platform Engineering
Team: Databricks AI & Data Platform / Snowflake Modern Data Platform
ROLE SUMMARY
Ecolab 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.
KEY RESPONSIBILITIES
- 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.