Lead Data & AI Ops Engineer, Data & AI Engineering

Pretium Enterprise Operations India Private Limited (PES India); Bengaluru; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

Pretium Enterprise Services (PES India) seeks a Lead Data & AI Operations Engineer to own end-to-end operational health and governance of enterprise Data & AI platforms in Bengaluru. You will establish operating models, automation, observability, and cost efficiency for Snowflake and data pipelines.

You will lead incident management, build automation with Python and RPA, and drive DataOps/MLOps through CI/CD practices, collaborating with Data Eng, Analytics, BI, Security, and Infra teams while

Qualifications

• 8+ years of experience in data platforms, data ops, or production operations with leadership.
• Deep Snowflake architecture, administration, SQL performance, and RBAC expertise.
• Strong track record in FinOps/cost optimization and data governance.
• Proven ability to design and build automation using Python, APIs, and RPA.

Responsibilities

• Own end-to-end Data & AI operations, governance, and reliability for enterprise platforms.
• Provide Snowflake production support, workload management, and health checks.
• Enhance data pipelines, ingestion, transformation, and orchestration across production environments.
• Support AI/ML workloads in production, monitoring jobs, data dependencies, and integrations.
• Develop and apply AI-assisted runbooks, RCA templates, and proactive health monitoring.
• Create automation via Python scripts, APIs, and RPA to reduce manual toil.
• Implement intelligent monitoring, anomaly detection, and automated remediation workflows.
• Lead DataOps, MLOps, and DevOps practices including CI/CD pipelines and release validation.
• Collaborate with Data Eng, Analytics, BI, Security, and Infra teams; mentor engineers.
• Participate in on-call rotations and ensure incidents meet SLA targets.

Skills

Snowflake expertise

Automation

Python scripting

RPA

APIs

Fivetran

Informatica

Azure Data Factory

CI/CD

DataOps

Observability

Data quality

Governance

Metadata & lineage

Production operations

Power BI

Looker

Leadership

Tools

Fivetran

Informatica

Azure Data Factory

Power BI

Looker

Job description

Role Overview

We are seeking a high-potential, hands-on Lead Data & AI Operations Engineer to own and continuously improve the operational health, governance, controls, reliability, and efficiency of our enterprise Data & AI ecosystem. This is a high-impact technical leadership role with end-to-end accountability for Data & AI Operations across the company. The successful candidate will establish the operating model, engineering controls, automation, observability, and governance required to run Data & AI platforms as reliable, secure, and cost-efficient enterprise services. The ideal candidate combines deep Snowflake and Data Engineering expertise with a strong operations and controls mindset. This engineer will also design, build, and deliver technical solutions and platform capabilities required to achieve operational excellence and efficiency goals.

Key Responsibilities

• Supported end-to-end Data & AI Operations and Production Support across enterprise data platforms, data pipelines, analytics, BI, and AI/ML workloads, ensuring availability, reliability, performance, and SLA adherence.
• Provided day-to-day Snowflake production support and administration, including workload monitoring, query performance analysis, troubleshooting, access/RBAC management, capacity monitoring, and platform health checks.
• Supported and enhanced Data Engineering pipelines and workflows, troubleshooting data ingestion, transformation, orchestration, processing, and downstream data delivery issues across production environments.
• Supported AI/ML and GenAI workloads in production, including monitoring application and model-related jobs, data dependencies, API integrations, scheduled processes, failures, and overall operational health.
• Contributed to AI Operations (AIOps) capabilities by using AI/GenAI tools for incident analysis, log summarization, anomaly identification, troubleshooting assistance, knowledge retrieval, and faster root-cause analysis.
• Developed Python scripts, APIs, workflow automation, RPA, and AI-assisted automation to reduce repetitive operational activities, automate health checks and validations, accelerate issue resolution, and improve support productivity.
• Supported the implementation of intelligent monitoring and anomaly detection across data pipelines, Snowflake workloads, and AI services to proactively identify failures, performance degradation, unusual patterns, and operational risks.
• Assisted in developing automated remediation and self-healing operational workflows for common production issues, reducing manual intervention and improving the Resolution SLA.
• Used GenAI-based operational assistants to support troubleshooting, incident summarization, RCA preparation, log analysis, runbook recommendations, and knowledge management activities.
• Monitored production data pipelines, ETL/ELT jobs, orchestration workflows, AI workloads, APIs, and platform services, investigated failures, performed impact analysis, and coordinated timely service restoration.
• Performed data quality checks, reconciliation, validation, and root-cause analysis to identify data discrepancies and ensure accurate, complete, and reliable data delivery to downstream applications and AI/analytics workloads.
• Supported enterprise data platform controls covering data quality, access, security, privacy, metadata, lineage, change management, and production readiness.
• Monitored Snowflake and cloud consumption, performance, and utilization, identified inefficient queries and workloads, and supported optimization initiatives to improve performance and control platform costs.
• Built and maintained observability, monitoring, alerting, operational dashboards, automated health checks, and proactive notifications across Data and AI platforms.
• Managed Incident, Problem, Change, and Release Management activities, including production troubleshooting, service restoration, RCA documentation, change validation, deployment support, and permanent remediation of recurring issues.
• Supported DataOps, MLOps, AIOps, and DevOps practices, including CI/CD pipelines, testing, deployment, release validation, version control, monitoring, documentation, and production support.
• Worked closely with Data Engineering, Analytics, BI, AI/ML, Architecture, Security, Infrastructure, and business teams to troubleshoot production issues, manage dependencies, and implement platform improvements.
• Participated in on-call and production support activities, ensuring critical Data and AI incidents were addressed within agreed SLAs and appropriately communicated to stakeholders.
• Identified recurring operational issues and implemented automation, AI-assisted solutions, process improvements, and permanent fixes to reduce manual effort, prevent repeat incidents, and improve production stability.
• Contributed to continuous improvement by promoting operational discipline, automation-first practices, documentation, reusable runbooks, knowledge sharing, and Data/AI production support best practices.

What We Are Looking For

• 8–12 years of experience across Data Engineering, Data Platforms, Data Ops, Cloud Engineering, or Production Operations, with demonstrated technical leadership.
• Deep hands-on Snowflake expertise, including architecture, administration, SQL, performance tuning, workload management, security/RBAC, monitoring, troubleshooting, and optimization.
• Strong experience designing and building engineering solutions, not just administering or supporting Data Platforms.
• Demonstrated FinOps and cost optimization experience, with measurable outcomes in Snowflake/cloud consumption reduction, workload optimization, cost attribution, and efficiency improvement.
• Strong experience building automation using RPA platforms, Python, APIs, workflow automation, and AI/GenAI tools.
• Strong expertise with dBT and enterprise ETL/ELT technologies such as Fivetran, Informatica, and Azure Data Factory.
• Experience implementing DataOps, CI/CD, observability, data quality, governance, metadata, lineage, and automated platform controls.
• Strong understanding of production operations, incident/problem management, RCA, change management, and platform reliability engineering.
• Experience with enterprise BI platforms such as Power BI and Looker.
• Ability to operate as both a hands-on engineer and technical leader/manager, taking problems from identification through solution architecture, engineering, implementation, and measurable business outcome.

NOTES

• Prefer candidates already residing in Bangalore.
• Standard Shift Timing is 12noon to 9pm, however this may vary depending on the business requirements.
• 3 Days work from office.
• Weekend on call support is required.

With expert business service capabilities, Pretium Enterprise Services (“PES”), is integral to the growth, innovation, and transformation of Pretium’s global operating companies. Partnering with internal and external businesses and stakeholders to rapidly unlock value for our customers, PES delivers with excellence and ownership, enabling scalable growth.