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
Actively participate in Agile ceremonies, including requirements refinement, sprint planning, effort estimation, sprint reviews, and retrospectives, to support successful delivery of platform capabilities.
Participate in technical design discussions, analyze requirements, and translate complex business needs into scalable data platform solutions.
Design, develop, and optimize core enterprise data platform capabilities, including data acquisition, ingestion, transformation, workflow orchestration, and reusable processing frameworks, leveraging Python, SQL, cloud-native technologies, and cloud data warehouse platforms to support large-scale, high-performance data processing.
Build and optimize large-scale data pipelines supporting data ingestion, transformation, validation, and distribution across enterprise cloud environments.
Implement metadata-driven processing frameworks and reusable engineering patterns that improve automation, standardization, scalability, and reuse.
Build and enhance platform capabilities related to metadata management, lineage, dependency tracking, data quality, governance, and workflow control.
Incorporate Agentic AI capabilities into platform services to enhance automation, intelligent decision-making, developer productivity, and operational efficiency.
Partner with cross-functional teams to support collaborative development, system integration, and end-to-end testing across platform components and dependent systems.
Develop and maintain system, integration, regression, and test automation frameworks to ensure platform quality, reliability, and production readiness.
Support deployment and release activities, ensuring successful rollout of platform enhancements and seamless integration with dependent systems and downstream consumers.
Contribute to engineering standards, architectural patterns, operational best practices, and framework conventions that improve platform maintainability, consistency, scalability, and supportability.
Provide L2/L3 production support for data platforms and products, perform root cause analysis, and implement corrective actions to ensure platform stability and operational excellence.
Drive continuous improvements in platform performance, scalability, resilience, observability, and operational efficiency across data workflows and framework components.
Lead Proof of Concepts (POCs) and technology evaluations to assess emerging technologies, validate architectural approaches, and identify opportunities for platform modernization and innovation.
Support product documentation by providing technical expertise, implementation details, and content validation to ensure accuracy, completeness, and alignment with platform capabilities.
Conduct live product demonstrations and participate in user workshops to showcase platform capabilities, gather feedback, validate solutions, and support platform adoption
Required Qualifications
Bachelor’s degree in Computer Science, Information Systems, or a related technical 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 data pipelines and workflows.
Strong proficiency in 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 and optimizing complex 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 with dbt, including data modeling, transformation, testing, documentation, and deployment within modern data platforms.
Proven track record of 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 environments, leveraging object storage, cloud data services, and major cloud platforms such as Azure, AWS, or GCP.
Exposure to Apache Iceberg, leveraging schema 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 Antigravity, leveraging AI-assisted coding, testing, and automation to improve engineering productivity and software delivery.
Understanding of Agentic 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 engineers, fostering technical 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
BlackRock’s 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, we’ve provided guidance on 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 children’s 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. It’s why we’re 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 Careers.BlackRock.com. 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.
Skills Required
- Bachelor's degree in Computer Science, Information Systems, or related technical field
- 10-14 years of experience in Data Engineering, Data Platform Engineering, or related software engineering role
- Hands-on experience with Apache Airflow for orchestrating complex data pipelines
- Strong proficiency in Python for developing scalable, production-grade data pipelines
- Hands-on experience designing and building ETL/ELT frameworks and reusable pipeline components
- Hands-on experience with Snowflake or equivalent cloud-native analytical data platforms
- Extensive hands-on experience writing and optimizing complex SQL (including UDFs and stored procedures)
- Proven experience in query optimization, performance tuning, and workload management for large-scale data environments
- Extensive hands-on experience with dbt (data modeling, transformation, testing, documentation, deployment)
- Hands-on experience with streaming technologies and messaging platforms (e.g., Snowpipe, Kafka)
- Hands-on experience building solutions in cloud-native and distributed environments (Azure, AWS, or GCP)
- Exposure to Apache Iceberg (schema evolution, partition evolution, time-travel)
- Experience with SDK and API-based development for integrating data platforms and services
- Hands-on experience designing and developing cloud-native data lake and data warehouse architectures
- Exposure to AI-native development tools (Windsurf, Cursor, Antigravity)
- Understanding of Agentic AI architectures, MCP, Vector Databases, and RAG concepts
- Experience with Docker, Kubernetes, and containerized deployment architectures
- Strong understanding of software engineering principles, Agile methodologies, Git-based source control, and CI/CD pipelines
- Experience implementing and managing metadata, lineage, dependency management, data quality, observability, and operational governance
- Proven ability to drive technical design decisions, decompose complex initiatives, mentor engineers, and collaborate with stakeholders
- Excellent verbal and written communication skills
BlackRock Compensation & Benefits Highlights
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Retirement Support — The package includes a 401(k)/Retirement Savings Plan with an employer match plus an additional company retirement contribution. Together with an employee stock purchase option, this positions retirement support as a notable strength for U.S. roles.
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Parental & Family Support — Paid parental leave and family‑building resources (adoption, fertility, nursing support) are emphasized, alongside child, special‑needs, and elder‑care resources. This breadth signals meaningful support across diverse family situations.
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Leave & Time Off Breadth — Flexible Time Off (FTO) for eligible roles is promoted, with additional sick and bereavement leave. Time away is framed as enabling rest and recharge when needed.
BlackRock Insights
What We Do
As the world’s largest asset manager, BlackRock partners with investors around the globe to help them (and those on whose behalf they invest) plan for life’s most important goals – like retirement, home ownership and their children’s education. Our clients range from governments, foundations and other large institutions to those investing on behalf of individuals, including firefighters, nurses, teachers and factory workers. BlackRock was founded with the idea of creating a better asset management firm — one that was purpose-driven, focused on clients and risk management, and propelled by data and technology. Our breakthrough Aladdin® platform is BlackRock’s technological backbone, helping investors see and manage their whole portfolios in one place – from constructing investments to monitoring risk and executing trades. Used by hundreds of external institutions around the world, Aladdin combines powerful analytics and a common language to help investment teams make faster, more informed decisions across public and private markets. It’s a key part of our business and one of the reasons we’re trusted to manage more assets than any other investment manager today. At BlackRock, we challenge conventions and raise the bar for what’s possible. We harness technology to unlock new solutions, simplify complexity, and deliver investment strategies that meet people where they are. Whether it’s retirement planning, wealth building or navigating market shifts, we’re here to help clients invest more easily, more affordably and with more choice as we chart a path toward financial well-being together. Learn more: Careers.BlackRock.com
Why Work With Us
Without our people, technology is irrelevant. When we combine the power of people with the power of technology, we amplify our ability to create better outcomes for our employees, clients, shareholders and society alike.
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Employees engage in a combination of remote and on-site work.
BlackRock has 25,000 employees across more than 100 offices in over 40 countries around the world.
