SENIOR MANAGER, DATA ENGINEERING PLATFORM

Ecolab | Data & AI Platform Engineering

Location: [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.

• Partner with Data Engineering, AI/ML Engineering, Security, Architecture, and business teams to align the platform roadmap with enterprise priorities.

REQUIRED QUALIFICATIONS

• 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.

PREFERRED QUALIFICATIONS

• 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.