Vice President Data Engineering Aladdin Data

Skills

About the Role

You will design, build, and optimize scalable data platforms and batch and real-time pipelines. You will create reusable frameworks and automation, enforce quality, security, governance, lineage, and observability standards, resolve production issues, improve performance and cost efficiency, influence architecture, mentor engineers, and drive engineering practices across teams.

Requirements

• Have at least seven years of experience delivering enterprise-scale data engineering solutions, including technical leadership.
• Have at least four years of Java, Python, or Scala programming experience, including Core Python, PySpark, UDFs, and pytest.
• Have at least four years of experience building and optimizing big-data pipelines, architectures, and datasets.
• Understand workflow tools such as Airflow, dbt, and Kafka.
• Have at least four years of production Spark experience, including parallel execution and resource and execution-mode selection.
• Have at least four years of experience with Hive on Spark, Yarn, Sqoop, bucketing, partitioning, tuning, ORC, Parquet, and Avro.
• Have at least four years of experience with Transact-SQL, Microsoft SQL Server, MySQL, NoSQL, and GraphQL.
• Have strong Snowflake implementation experience.
• Use Great Expectations for data quality and automated validation.
• Use Swagger or OpenAPI to design, document, and test RESTful APIs.
• Have experience with AWS or Azure, OpenStack, Docker, Kafka, and Kubernetes deployment, maintenance, or administration.
• Understand CI/CD tools such as Jenkins, GitLab CI, or Azure DevOps.
• Have experience with data governance, metadata management, lineage, Axon, and Unity Catalog.
• Manage business glossaries, access controls, auditing, and centralized governance across cloud and hybrid data assets.
• Have hands-on experience with Databricks notebooks, workflows, and machine-learning integrations.
• Collaborate effectively across global cross-functional teams.
• Preferably understand machine learning, artificial intelligence, generative AI, and AI-ready data-platform patterns.

Responsibilities

• Design, develop, and maintain scalable, reliable, high-performance data pipelines.
• Build and optimize batch and real-time ingestion, transformation, and publishing processes.
• Develop reusable data engineering frameworks, components, and automation.
• Ensure data products satisfy quality, security, governance, lineage, and observability standards.
• Segregate data according to applicable policies.
• Improve platform performance, scalability, resilience, and cost efficiency.
• Troubleshoot production issues, conduct root-cause analysis, and implement sustainable solutions.
• Contribute to engineering standards, code reviews, testing, CI/CD automation, and technical documentation.
• Automate manual ingestion and optimize data delivery against service-level agreements.
• Collaborate on infrastructure redesigns for greater scalability.
• Evaluate emerging technologies and recommend improvements to the data ecosystem.
• Provide technical leadership for complex initiatives.
• Mentor junior engineers.
• Influence architecture decisions and drive engineering practices across teams.

Benefits

• Retirement plan
• Tuition reimbursement
• Comprehensive healthcare
• Support for working parents
• Flexible Time Off