Data Ops Engineer

Pfizer

Senior Manager in DataOps leading architecture and implementation of AI solutions at Pfizer. Focused on developing data science workflows and managing analytics platforms supporting AI/ML.

Posted 7/2/2026full-timeChennai • 🇮🇳 IndiaSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and operating data and analytics platforms, ensuring data reliability and performance across analytics and AI pipelines. Proficient in DataOps automation, cloud-native deployment, and implementing data observability and monitoring practices.

Highest-signal resume keywords

Data EngineeringDataOps AutomationAWS/Azure EnvironmentsModern Data Processing FrameworksData Pipeline Orchestration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DevelopmentCI/CD ImplementationAutomated Data Quality TestingPython ProgrammingSQL ProficiencySpark FrameworkScala ProgrammingBash ScriptingData Transformation ToolsPerformance Testing

Tools & Technologies

Data Observability ToolsMonitoring SolutionsInfrastructure-as-CodeConfiguration Management

Industry Keywords

Data GovernanceAccess ControlCompliance-Aware EnvironmentsBatch Data IngestionStreaming Data Ingestion

Tech Stack

Tools & technologies

AWSAzureCloudPythonScalaSparkSQL

About the role

Key responsibilities & impact

Lead the design, build, and operation of data and analytics platforms supporting commercial reporting, advanced analytics, and AI/ML use cases.
Own operational pipelines for batch and streaming data ingestion, transformation, and serving, ensuring reliability, scalability, and performance.
Implement and maintain DataOps automation using CI/CD, infrastructure-as-code, and configuration management to support analytics and ML workloads.
Partner with infrastructure and platform teams to ensure data platforms are deployed using standardized cloud-native patterns (AWS/Azure).
Own end-to-end data reliability, including freshness, completeness, accuracy, and availability across analytics and AI pipelines.
Implement data observability and monitoring capabilities (e.g., pipeline health, schema drift, SLA/SLO tracking).

Requirements

What you’ll need

8+ years of experience in data engineering, analytics engineering, or DataOps roles.
Strong hands-on experience building and operating production data pipelines in AWS or Azure environments.
Proven expertise in: Modern data processing frameworks (e.g., Spark, SQL-based transformation tools)
CI/CD and automation for data platforms
Data pipeline orchestration and monitoring
Solid understanding of testing and quality practices for data systems, including: Automated data quality testing
Pipeline validation and regression testing
Supporting non-functional testing (performance, reliability, scalability)
Experience implementing data observability, monitoring, and incident management practices.
Demonstrated experience with secure data handling and governance, including access control and compliance-aware environments.
Proficiency in programming and scripting (e.g., Python, SQL, Scala, Bash).

Benefits

Comp & perks

Professional development opportunities
Flexible working hours
Health insurance
Paid time off