{"id":1666207,"url":"https://alion.io/job/blackrock-vp-senior-data-engineer-pythonsnowflakeapache-airflow","title":"VP - Senior Data Engineer (Python/Snowflake/Apache Airflow)","company":{"id":5262,"name":"BlackRock","domain":"blackrock.com","url":"https://alion.io/company/blackrock","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":88,"open_postings":3,"ghost_share":0,"stale_share":0.667,"repost_share":0,"time_to_fill_p50_days":12,"computed_at":"2026-10-13T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"head","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mumbai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":24000,"max_usd":57000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":85},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Airflow","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Cursor","optional":false},{"name":"dbt","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"Kubernetes","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Windsurf","optional":false}],"status":"live","first_seen_at":"2026-09-25T00:00:00Z","employer_posted_date":"2026-09-25","last_verified_at":"2026-10-13T11:55:30Z","board_verified":true,"closed_at":null,"days_open":18,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":18},"description":"About this role\nThe 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.\nResponsibilities\n Actively participatein Agile ceremonies, including requirementsrefinement, sprint planning, effort estimation, sprint reviews, and retrospectives, to support successful delivery of platform capabilities.\n\nParticipate in technical design discussions, analyze requirements, and translate complex business needs into scalable data platform solutions.\n\n Design, develop, and optimizecore enterprise data platform capabilities, including data acquisition, ingestion, transformation, workflow orchestration, and reusable processing frameworks, leveragingPython, SQL, cloud-native technologies, and cloud data warehouse platforms to support large-scale, high-performance data processing.\n\nBuild and optimizelarge-scale data pipelines supporting data ingestion, transformation, validation, and distribution across enterprise cloud environments.\n\n Implement metadata-driven processing frameworks and reusable engineering patterns that improve automation, standardization, scalability, and reuse.\n\nBuild and enhance platform capabilities related to metadata management, lineage, dependency tracking, data quality, governance, and workflow control.\n\nIncorporate Agentic AI capabilities into platform services to enhance automation, intelligent decision-making, developer productivity, and operational efficiency.\n\n Partner with cross-functional teams to support collaborative development, system integration, and end-to-end testing across platform components and dependent systems.\n\nDevelop and maintainsystem, integration, regression, and test automation frameworks to ensure platform quality, reliability, and production readiness.\n\nSupport deployment and release activities, ensuring successful rollout of platform enhancements and seamless integration with dependent systems and downstream consumers.\n\nContribute to engineering standards, architectural patterns, operational best practices, and framework conventions that improve platform maintainability, consistency, scalability, and supportability.\n\nProvide L2/L3 production support for data platforms and products, perform root cause analysis, and implement corrective actions to ensure platform stability and operational excellence.\n\n Drive continuous improvements in platform performance, scalability, resilience, observability, and operational efficiency across data workflows and framework components.\n\nLead Proof of Concepts (POCs) and technology evaluations to assess emerging technologies, validatearchitectural approaches, and identifyopportunities for platform modernization and innovation.\n\nSupport product documentation by providing technical expertise, implementation details, and content validation to ensure accuracy, completeness, and alignment with platform capabilities.\n\nConduct live product demonstrations and participatein user workshops to showcaseplatform capabilities, gather feedback, validatesolutions, and support platform adoption\n\nRequired Qualifications\nBachelor’s degree in Computer Science, Information Systems, or a relatedtechnical field.\n\n8+ years of experience in Data Engineering, Data Platform Engineering, or a related software engineering role.\n\nHands-on experience with Apache Airflow for orchestrating complex, dependency-driven data pipelinesand workflows.\n\nStrong proficiencyin Python, with hands-on experience developing scalable, production-grade data pipelines and frameworks.\n\nHands-on experience designing and building ETL/ELT frameworks, reusable data pipeline components, and workflow-driven data systems.\n\nHands-on experience with Snowflake or equivalent cloud-native analytical data platforms.\n\nStrong hands-on experience writing and optimizingcomplex SQL, including stored procedures, UDFs, and performance-critical queries.\n\nProven experience in query optimization, performance tuning, and workload management for large-scale data environments.\n\nExtensive hands-on experience with dbt, including data modeling, transformation, testing, documentation, and deployment within modern data platforms.\n\nProven track recordof improving the performance, reliability, scalability, and maintainability of enterprise data platforms and pipelines.\n\nHands-on experience with streaming technologies (e.g., Snowpipe, Kafka) and messaging platforms for building scalable, real-time data processing solutions.\n\nHands-on experience building solutions in cloud-native and distributed environments, leveragingobject storage, cloud data services, and major cloud platforms such as Azure, AWS, or GCP.\n\nExposure to Apache Iceberg, leveragingschema evolution, partition evolution, and time-travel capabilities to build scalable and reliable data lake solutions.\n\nExperience with SDK and API-based development, enabling seamless integration of data platforms, cloud services, and enterprise applications through secure and scalable interfaces.\n\nHands-on experience designing and developing cloud-native data platforms, including Data Lake and Data Warehouse architectures, using modern data engineering best practices.\n\nExposure to AI-native development tools such as Windsurf, Cursor, and Antigravity, leveragingAI-assisted coding, testing, and automation to improve engineering productivity and software delivery.\n\nUnderstanding ofAgentic AI architectures, including Skills, Model Context Protocol (MCP), Vector Databases, Retrieval-Augmented Generation (RAG), and AI Agent orchestration concepts.\n\nExperience working with Docker, Kubernetes, and containerized deployment architectures in modern engineering environments.\n\nStrong 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.\n\nExperience implementing and managing metadata, lineage, dependency management, data quality controls, observability, and operational governance within enterprise data ecosystems.\n\nExperience driving technical design decisions, decomposing complex initiatives, mentoring engineers, and collaborating effectively with cross-functional engineering and business stakeholders.\n\nExcellent verbal and written communication skills, with the ability to articulate complex technical concepts to both technical and non-technical audiences.\n\nStrong 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.\n\nAdaptable and collaborative team player with a continuous learning mindset and passion for emerging technologies.\n\nProven ability to mentor and guide engineers, fosteringtechnical excellence, knowledge sharing, and engineering best practices across teams.\n\nOur benefits\nTo 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.\nOur hybrid work model\nBlackRock’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.\n\nGuidance on AI use for candidates\nAt 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.\nAbout BlackRock\nAt 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.\nThis 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.\nTo 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.\nBlackRock is proud to be an Equal Opportunity Employer. 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