Lead Data & AI Operations Engineer

Company Overview

Pretium is a specialized investment manager with $62B in assets under management (AUM) focused on real estate, mortgage finance, and corporate debt. Pretium manages more than 1,75,000 single-family rentals (SFRs) and multi-family rentals in the United States and has a global employee base of 7,000.

Pretium Enterprise Operations India Private Limited (PES India), based in Bangalore, is integral to the growth, innovation, and transformation of Pretium's global operating companies. With 700+ employees, PES India delivers with excellence, ownership, and a relentless focus on scalable growth.

About the Role

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.

• 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.

Perks & Benefits:

We believe great people deserve to be taken care of – here's what working at PES India looks like beyond the job:

• Competitive Compensation
• Shift & Weekend Allowances
• Insurance Coverage
• Hybrid Flexibility
• Career Growth