Job Description – Lead Platform Engineer, Data Observability & Reliability

We are looking for a seasoned and hands-on Lead Platform Engineer to help build the next generation of observability, reliability, and intelligent automation capabilities for enterprise data platforms. This role sits within the Data & Analytics organization and is ideal for a strong engineer who enjoys solving complex platform challenges, building scalable solutions, using cloud-native technologies, and applying AI-driven engineering practices to improve speed, quality, and operational excellence.

As a Lead Platform Engineer, you will architect, design, and lead the implementation of solutions that monitor data pipelines, improve data quality, detect anomalies, reduce operational risk, optimize cloud cost, and increase trust in enterprise data products. You will work closely with data engineering, platform, operations, product, and business teams to create reliable, scalable, and intelligent observability capabilities.

At Tyson Foods, this role provides the opportunity to solve complex enterprise-scale data platform challenges by designing and building next-generation observability, reliability, and automation capabilities using modern cloud technologies, AI, and intelligent automation. You will be part of a culture that values innovation, ownership, engineering excellence, continuous learning, and career growth.

Primary Job Responsibilities:

Lead the vision and roadmap for leveraging AI and intelligent automation to transform data reliability, observability, and data platform operations across the enterprise.
Architect scalable frameworks and solutions for real-time monitoring, logging, alerting, anomaly detection, data quality tracking, and operational insights.
Design and build next-generation observability capabilities leveraging AI, metadata intelligence, lineage, and telemetry data to proactively detect issues, accelerate root cause analysis, assess business impact, and automate platform operations.
Evaluate, prototype, and introduce emerging technologies, AI frameworks, and modern observability approaches to detect schema drift, data drift, freshness degradation, volume anomalies, reconciliation issues, and other operational risks.
Collaborate with data engineering, platform, operations, architecture, product, and governance teams to define observability requirements and deliver scalable reliability solutions.
Define and operationalize reliability KPIs, service health indicators, and observability metrics for critical data products and platforms.
Lead root cause analysis for data reliability incidents and implement preventive actions to reduce recurrence.
Optimize the performance, scalability, reliability, resilience, and cost efficiency of observability platforms and supporting data infrastructure.
Drive adoption of observability best practices across teams through documentation, playbooks, dashboards, training materials, and enablement sessions.
Ensure observability solutions align with enterprise standards for security, compliance, governance, availability, and operational excellence.
Mentor engineers, promote engineering best practices, and provide technical leadership across solution design, development, automation, and operational readiness.
Drive continuous improvement by identifying opportunities to modernize platforms, enhance developer productivity through AI-assisted engineering practices, and improve the reliability of enterprise data products.

Qualifications:

8+ years of experience in platform engineering, cloud engineering, data engineering, DevOps, or similar technology roles.
Strong experience designing, building, and operating solutions on cloud platforms such as GCP or AWS
Deep understanding of cloud infrastructure, cloud operations, distributed systems, and enterprise-scale platform reliability.
Strong programming and scripting experience using technologies such as Python, SQL, Bash, or similar languages.
Experience with containerization and orchestration technologies such as Docker and Kubernetes.
Experience with infrastructure as code and automation tools such as Terraform, CloudFormation, Ansible, or equivalent.
Experience with CI/CD pipelines, DevOps practices, release automation, and engineering productivity improvements.
Demonstrated ability to leverage AI-assisted engineering and intelligent automation to improve development velocity, code quality, testing, documentation, troubleshooting, and operational efficiency.
Experience leading technical design discussions, providing architectural guidance, and driving cross-functional engineering initiatives.
Strong analytical, troubleshooting, and problem-solving skills with the ability to resolve complex technical issues.
Proven ability to mentor engineers, influence stakeholders, and drive projects from concept to implementation.
Excellent communication skills with the ability to explain technical concepts clearly to engineering, product, operations, and leadership teams.
Familiarity with Agile delivery practices and modern software engineering ways of working.

Good to have:

Strong understanding of modern cloud-based data platforms and analytics ecosystems on GCP or AWS, including data ingestion, replication, transformation, and processing technologies such as Fivetran, SAP HANA/SLT, dbt, or similar tools.
Experience with data observability and monitoring platforms such as Monte Carlo, Bigeye, OpenMetadata, Acceldata, Datadog, Dynatrace, New Relic, Grafana, Prometheus, or equivalent solutions.
Experience building dashboards, operational scorecards, alerting frameworks, service health indicators, and observability metrics to measure platform reliability and performance.
Familiarity with business intelligence and reporting tools such as Power BI.
Experience leveraging AI-powered development and automation tools, including Microsoft Copilot, Claude Code, GitHub Copilot, or similar solutions to improve engineering productivity and solution delivery.

Tyson Foods India

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