Data Engineering Manager

Experity; Bengaluru; Sur place; INR 4 000 000 - 7 000 000; Plein temps

Résumé du poste

Experity seeks an experienced technical leader to build and guide a high-performing engineering organization in Bengaluru. You will own onboarding, performance management, and career growth, while shaping scalable data pipelines and cloud platforms using Snowflake, AWS, and Airflow.

You will drive DataOps, CI/CD, and secure, auditable data services across a globally distributed Data & Analytics team. The role emphasizes leadership, architectural judgment, and collaboration with Product,

Qualifications

• Bachelor’s degree in CS/Engineering/IS or equivalent practical experience.
• 10+ years in software or data engineering, including large-scale cloud data platforms; 3+ years of direct engineering people management.
• Strong hands-on SQL and Python; experience with Snowflake, AWS, ETL/ELT, data modeling, distributed systems, and batch/stream processing.
• Experience with Airflow, dbt, CI/CD, monitoring, automated testing, and infrastructure-as-code; familiarity with Kafka and CDC.
• Proven architectural judgment, production troubleshooting, engineering delivery leadership, and stakeholder communication.
• Understanding of security, governance, and reliability in sensitive data environments; ability to remain technically engaged while delegating effectively.

Responsabilités

• Build, hire, coach, and develop a high-performing engineering team; own onboarding, performance management, and career growth.
• Set clear goals and expectations; foster ownership, accountability, collaboration, and continuous learning.
• Develop technical leaders through feedback, mentoring, design reviews, and knowledge sharing.
• Lead the evolution of scalable, reliable, secure, and cost-effective data pipelines and cloud data platforms.
• Provide direction across ingestion, CDC, ETL/ELT, orchestration, modeling, storage, and serving layers using Snowflake, AWS, Kafka, dbt, and Airflow.
• Partner with architects and Staff Engineers on design reviews, trade-offs, modernization, technical debt, and batch/near-real-time processing.
• Establish standards for code quality, automated testing, observability, maintainability, and AI-ready data services.
• Own engineering execution, capacity planning, dependencies, risks, and delivery commitments while partnering with Product and technical leaders on roadmap planning and prioritization.
• Champion DataOps, CI/CD, infrastructure-as-code, automated testing, and safe deployment practices.
• Own pipeline reliability, incident response, root cause analysis, and prevention; track freshness, quality, availability, cost, and delivery predictability.
• Drive data quality, reconciliation, validation, lineage, and monitoring across the platform.
• Partner with governance, architecture, and security teams on HIPAA compliance, least-privilege access, auditability, and appropriate data protection.
• Enable trusted, reusable models and data products for self-service analytics and AI applications.
• Champion responsible AI-native development for coding, testing, documentation, troubleshooting, and reviews with human validation.
• Partner with Analytics, Data Science, and ML Engineering on AI-ready datasets and reusable data services.
• Evaluate technologies and automation opportunities that improve engineering productivity and platform efficiency.
• Partner with Product, Engineering, Analytics, AI/ML, Security, and business stakeholders to translate needs into solutions.
• Operate as an integrated member of a globally distributed Data & Analytics team, fostering collaboration, shared ownership, and alignment while clearly communicating progress, technical trade-offs, risks, and decisions.

Connaissances

Leadership

People management

Cross-functional collaboration

Mentoring

Communication

Formation

Bachelor's degree in CS/Engineering/IS

Outils

Snowflake

AWS

Airflow

dbt

Kafka

CI/CD

Monitoring

Automated testing

Infrastructure as code

CDC

Description du poste

Key Responsibilities

Engineering Leadership & People Management

• Build, hire, coach, and develop a high-performing engineering team; own onboarding, performance management, and career growth.
• Set clear goals and expectations; foster ownership, accountability, collaboration, and continuous learning.
• Develop technical leaders through feedback, mentoring, design reviews, and knowledge sharing.

Data Platform Architecture & Technical Excellence

• Lead the evolution of scalable, reliable, secure, and cost-effective data pipelines and cloud data platforms.
• Provide direction across ingestion, CDC, ETL/ELT, orchestration, modeling, storage, and serving layers using Snowflake, AWS, Kafka, dbt, and Airflow.
• Partner with architects and Staff Engineers on design reviews, trade-offs, modernization, technical debt, and batch/near-real-time processing.
• Establish standards for code quality, automated testing, observability, maintainability, and AI-ready data services.

Execution, Delivery & Operational Excellence

• Own engineering execution, capacity planning, dependencies, risks, and delivery commitments while partnering with Product and technical leaders on roadmap planning and prioritization.
• Champion DataOps, CI/CD, infrastructure-as-code, automated testing, and safe deployment practices.
• Own pipeline reliability, incident response, root cause analysis, and prevention; track freshness, quality, availability, cost, and delivery predictability.

Data Quality, Governance & Security

• Drive data quality, reconciliation, validation, lineage, and monitoring across the platform.
• Partner with governance, architecture, and security teams on HIPAA compliance, least-privilege access, auditability, and appropriate data protection.
• Enable trusted, reusable models and data products for self-service analytics and AI applications.

AI-Native Engineering & Innovation

• Champion responsible AI-native development for coding, testing, documentation, troubleshooting, and reviews with human validation.
• Partner with Analytics, Data Science, and ML Engineering on AI-ready datasets and reusable data services.
• Evaluate technologies and automation opportunities that improve engineering productivity and platform efficiency.

Cross-Functional Collaboration

• Partner with Product, Engineering, Analytics, AI/ML, Security, and business stakeholders to translate needs into solutions.
• Operate as an integrated member of a globally distributed Data & Analytics team, fostering collaboration, shared ownership, and alignment while clearly communicating progress, technical trade-offs, risks, and decisions.

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field, or equivalent practical experience.
• 10+ years in software or data engineering, including large-scale cloud data platforms; 3+ years of direct engineering people management.
• Strong hands-on SQL and Python; experience with Snowflake, AWS, ETL/ELT, data modeling, distributed systems, and batch/stream processing.
• Experience with Airflow, dbt, CI/CD, monitoring, automated testing, and infrastructure-as-code; familiarity with Kafka and CDC.
• Proven architectural judgment, production troubleshooting, engineering delivery leadership, and stakeholder communication.
• Understanding of security, governance, and reliability in sensitive data environments; ability to remain technically engaged while delegating effectively.

Preferred Qualifications

• Healthcare technology, SaaS, or regulated-industry experience.
• Snowflake optimization, cost management, dimensional modeling, semantic layers, and self-service analytics.
• AI/ML data infrastructure, and practical AI-native SDLC adoption.
• Experience leading distributed teams across India and U.S. time zones

Business Impact & Success Measures

• Platform reliability, freshness, incident prevention, and recovery time.
• Predictable delivery, improved engineering productivity, and reduced technical bottlenecks.
• Data quality, reconciliation, observability, governance, and trusted analytics.
• Cloud cost efficiency, scalability, automation, and responsible AI-assisted development.
• Team growth, engagement, clear ownership, and timely delivery of customer and AI-enabling data products.

Leadership Expectations

The successful candidate is both a technical leader and a people manager: able to dive into difficult problems, empower engineers to own solutions, make pragmatic trade-offs, and remain accountable for team and operational outcomes. The manager owns people, execution, and reliability while partnering with Staff Engineers on deep technical direction

Equal Opportunity Employer

Equal Opportunity Employer
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