VP Senior Data Engineer (PythonSnowflakeApache Airflow)

BlackRock

Job Location:

Mumbai - India

Monthly Salary: Not Disclosed

Posted on: 4 days ago

Vacancies: 1 Vacancy

Job Summary

About this role

The Senior Data Engineer (VP) designs develops and supports enterprise-scale data platform capabilities using Airflow dbt Python SQL cloud-native technologies and cloud data warehouse platforms to deliver scalable reliable and high-performance data solutions. The role focuses on building data pipelines reusable frameworks metadata-driven processing data quality and governance capabilities workflow orchestration and platform automation while leveraging emerging technologies such as Agentic AI to enhance engineering productivity and operational efficiency. Working closely with cross-functional teams the engineer delivers end-to-end platform solutions supports production operations drives continuous improvements in platform performance and reliability contributes to engineering best practices and platform modernization initiatives and mentors junior engineers to foster technical excellence and continuous learning.

Responsibilities

Activelyparticipatein Agile ceremonies includingrequirementsrefinement sprint planning effort estimation sprint reviews and retrospectives to support successful delivery of platform capabilities.

Participatein technical design discussions analyze requirements and translate complex business needs into scalable data platform solutions.

Design develop andoptimizecore enterprise data platform capabilities including data acquisition ingestion transformation workflow orchestration and reusable processing frameworksleveragingPython SQL cloud-native technologies and cloud data warehouse platforms to support large-scale high-performance data processing.

Buildandoptimizelarge-scale data pipelines supporting data ingestion transformation validation and distribution across enterprise cloud environments.

Implementmetadata-driven processing frameworks and reusable engineering patterns that improve automation standardization scalability and reuse.

Buildand enhance platform capabilities related to metadata management lineage dependency tracking data quality governance and workflow control.

IncorporateAgentic AI capabilities into platform services to enhance automation intelligent decision-making developer productivity and operational efficiency.

Partnerwith cross-functional teams to support collaborative development system integration and end-to-end testing across platform components and dependent systems.

Developandmaintainsystem integration regression and test automation frameworks to ensure platform quality reliability and production readiness.

Supportdeployment and release activities ensuring successful rollout of platform enhancements and seamless integration with dependent systems and downstream consumers.

Contributeto engineering standards architectural patterns operational best practices and framework conventions that improve platform maintainability consistency scalability and supportability.

ProvideL2/L3 production support for data platforms and products perform root cause analysis and implement corrective actions to ensure platform stability and operational excellence.

Drivecontinuous improvements in platform performance scalability resilience observability and operational efficiency across data workflows and framework components.

LeadProof of Concepts (POCs) and technology evaluations to assess emerging technologiesvalidatearchitectural approaches andidentifyopportunities for platform modernization and innovation.

Supportproduct documentation by providing technicalexpertise implementation details and content validation to ensure accuracy completeness and alignment with platform capabilities.

Conductlive product demonstrations andparticipatein user workshops toshowcaseplatform capabilities gather feedbackvalidatesolutions and support platform adoption

Required Qualifications

Bachelors degree in Computer ScienceInformation Systems ora relatedtechnical field.

8 years of experience in Data Engineering Data Platform Engineering or a related software engineering role.

Hands-on experience with Apache Airflow for orchestrating complex dependency-driven datapipelinesand workflows.

Strongproficiencyin Python with hands-on experience developing scalable production-grade data pipelines and frameworks.

Hands-on experience designing and building ETL/ELT frameworks reusable data pipeline components and workflow-driven data systems.

Hands-on experience with Snowflake or equivalent cloud-native analytical data platforms.

Strong hands-on experience writing andoptimizingcomplex SQL including stored procedures UDFs and performance-critical queries.

Proven experience in query optimization performance tuning and workload management for large-scale data environments.

Extensive hands-on experience withdbt including data modeling transformation testing documentation and deployment within modern data platforms.

Proventrack recordof improving the performance reliability scalability and maintainability of enterprise data platforms and pipelines.

Hands-on experience with streaming technologies (e.g.Snowpipe Kafka) and messaging platforms for building scalable real-time data processing solutions.

Hands-on experience building solutions in cloud-native and distributed environmentsleveragingobject storage cloud data services and major cloud platforms such as Azure AWS or GCP.

Exposure to Apache Icebergleveragingschema evolution partition evolution and time-travel capabilities to build scalable and reliable data lake solutions.

Experience with SDK and API-based development enabling seamless integration of data platforms cloud services and enterprise applications through secure and scalable interfaces.

Hands-on experience designing and developing cloud-native data platforms including Data Lake and Data Warehouse architectures using modern data engineering best practices.

Exposure to AI-native development tools such as Windsurf Cursor and AntigravityleveragingAI-assisted coding testing and automation to improve engineering productivity and software delivery.

Understanding ofAgentic AI architectures including Skills Model Context Protocol (MCP) Vector Databases Retrieval-Augmented Generation (RAG) and AI Agent orchestration concepts.

Experience working with Docker Kubernetes and containerized deployment architectures in modern engineering environments.

Strong understanding of software engineering principles scalable system design and modern development practices including Agile methodologies Git-based source control CI/CD pipelines code reviews and production-grade development standards.

Experience implementing and managing metadata lineage dependency management data quality controls observability and operational governance within enterprise data ecosystems.

Experience driving technical design decisions decomposing complex initiatives mentoring engineers and collaborating effectively with cross-functional engineering and business stakeholders.

Excellent verbal and written communication skills with the ability to articulate complex technical concepts to both technical and non-technical audiences.

Strong analytical critical-thinking and problem-solving skills with a proactive solution-oriented mindset and the ability to apply innovative out-of-the-box thinking to solve complex challenges.

Adaptable and collaborative team player with a continuous learning mindset and passion for emerging technologies.

Proven ability to mentor and guide engineersfosteringtechnical excellence knowledge sharing and engineering best practices across teams.

Our benefits

To help you stay energized engaged and inspired we offer a wide range of benefits including a strong retirement plan tuition reimbursement comprehensive healthcare support for working parents and Flexible Time Off (FTO) so you can relax recharge and be there for the people you care about.

Our hybrid work model

BlackRocks hybrid work model is designed to enable a culture of collaboration and apprenticeship that enriches the experience of our employees while supporting flexibility for all. Employees are currently required to work at least 4 days in the office per week with the flexibility to work from home 1 day a week. Some business groups may require more time in the office due to their roles and responsibilities. We remain focused on increasing the impactful moments that arise when we work together in person aligned with our commitment to performance and innovation. As a new joiner you can count on this hybrid model to accelerate your learning and onboarding experience here at BlackRock.

Guidance on AI use for candidates

At BlackRock AI has long been part of how we work enhancing decision-making improving operations and helping us deliver better outcomes for clients. We encourage candidates to use AI thoughtfully to learn prepare and work more effectively; but during our interview process we want to focus on getting to know you through your own experiences thinking and judgment. To support you weve provided guidanceon when and how to use AI during our hiring process so you can approach each step with confidence and showcase your best self.

About BlackRock

At BlackRock we are all connected by one mission: to help more and more people experience financial well-being. Our clients and the people they serve are saving for retirement paying for their childrens educations buying homes and starting businesses. Their investments also help to strengthen the global economy: support businesses small and large; finance infrastructure projects that connect and power cities; and facilitate innovations that drive progress.

This mission would not be possible without our smartest investment the one we make in our employees. Its why were dedicated to creating an environment where our colleagues feel welcomed valued and supported with networks benefits and development opportunities to help them thrive.

To learn more about BlackRock please visit . We also encourage you to get to know us on LinkedIn Instagram YouTube X and TikTok.

BlackRock is proud to be an Equal Opportunity Employer. We evaluate qualified applicants without regard to age disability family status gender identity race religion sex sexual orientation and other protected attributes at law.

Required Experience:

Exec

About this roleThe Senior Data Engineer (VP) designs develops and supports enterprise-scale data platform capabilities using Airflow dbt Python SQL cloud-native technologies and cloud data warehouse platforms to deliver scalable reliable and high-performance data solutions. The role focuses on build...

About this role

Responsibilities

Activelyparticipatein Agile ceremonies includingrequirementsrefinement sprint planning effort estimation sprint reviews and retrospectives to support successful delivery of platform capabilities.

Participatein technical design discussions analyze requirements and translate complex business needs into scalable data platform solutions.

Design develop andoptimizecore enterprise data platform capabilities including data acquisition ingestion transformation workflow orchestration and reusable processing frameworksleveragingPython SQL cloud-native technologies and cloud data warehouse platforms to support large-scale high-performance data processing.

Buildandoptimizelarge-scale data pipelines supporting data ingestion transformation validation and distribution across enterprise cloud environments.

Implementmetadata-driven processing frameworks and reusable engineering patterns that improve automation standardization scalability and reuse.

Buildand enhance platform capabilities related to metadata management lineage dependency tracking data quality governance and workflow control.

IncorporateAgentic AI capabilities into platform services to enhance automation intelligent decision-making developer productivity and operational efficiency.

Partnerwith cross-functional teams to support collaborative development system integration and end-to-end testing across platform components and dependent systems.

Developandmaintainsystem integration regression and test automation frameworks to ensure platform quality reliability and production readiness.

Supportdeployment and release activities ensuring successful rollout of platform enhancements and seamless integration with dependent systems and downstream consumers.

Contributeto engineering standards architectural patterns operational best practices and framework conventions that improve platform maintainability consistency scalability and supportability.

ProvideL2/L3 production support for data platforms and products perform root cause analysis and implement corrective actions to ensure platform stability and operational excellence.

Drivecontinuous improvements in platform performance scalability resilience observability and operational efficiency across data workflows and framework components.

LeadProof of Concepts (POCs) and technology evaluations to assess emerging technologiesvalidatearchitectural approaches andidentifyopportunities for platform modernization and innovation.

Supportproduct documentation by providing technicalexpertise implementation details and content validation to ensure accuracy completeness and alignment with platform capabilities.

Conductlive product demonstrations andparticipatein user workshops toshowcaseplatform capabilities gather feedbackvalidatesolutions and support platform adoption

Required Qualifications

Bachelors degree in Computer ScienceInformation Systems ora relatedtechnical field.

8 years of experience in Data Engineering Data Platform Engineering or a related software engineering role.

Hands-on experience with Apache Airflow for orchestrating complex dependency-driven datapipelinesand workflows.

Strongproficiencyin Python with hands-on experience developing scalable production-grade data pipelines and frameworks.

Hands-on experience designing and building ETL/ELT frameworks reusable data pipeline components and workflow-driven data systems.

Hands-on experience with Snowflake or equivalent cloud-native analytical data platforms.

Strong hands-on experience writing andoptimizingcomplex SQL including stored procedures UDFs and performance-critical queries.

Proven experience in query optimization performance tuning and workload management for large-scale data environments.

Extensive hands-on experience withdbt including data modeling transformation testing documentation and deployment within modern data platforms.

Proventrack recordof improving the performance reliability scalability and maintainability of enterprise data platforms and pipelines.

Hands-on experience with streaming technologies (e.g.Snowpipe Kafka) and messaging platforms for building scalable real-time data processing solutions.

Hands-on experience building solutions in cloud-native and distributed environmentsleveragingobject storage cloud data services and major cloud platforms such as Azure AWS or GCP.

Exposure to Apache Icebergleveragingschema evolution partition evolution and time-travel capabilities to build scalable and reliable data lake solutions.

Experience with SDK and API-based development enabling seamless integration of data platforms cloud services and enterprise applications through secure and scalable interfaces.

Hands-on experience designing and developing cloud-native data platforms including Data Lake and Data Warehouse architectures using modern data engineering best practices.

Exposure to AI-native development tools such as Windsurf Cursor and AntigravityleveragingAI-assisted coding testing and automation to improve engineering productivity and software delivery.

Understanding ofAgentic AI architectures including Skills Model Context Protocol (MCP) Vector Databases Retrieval-Augmented Generation (RAG) and AI Agent orchestration concepts.

Experience working with Docker Kubernetes and containerized deployment architectures in modern engineering environments.

Strong understanding of software engineering principles scalable system design and modern development practices including Agile methodologies Git-based source control CI/CD pipelines code reviews and production-grade development standards.

Experience implementing and managing metadata lineage dependency management data quality controls observability and operational governance within enterprise data ecosystems.

Experience driving technical design decisions decomposing complex initiatives mentoring engineers and collaborating effectively with cross-functional engineering and business stakeholders.

Excellent verbal and written communication skills with the ability to articulate complex technical concepts to both technical and non-technical audiences.

Strong analytical critical-thinking and problem-solving skills with a proactive solution-oriented mindset and the ability to apply innovative out-of-the-box thinking to solve complex challenges.