Lead Full Stack Data Engineer - Vice President

Citi•Mississauga, ON

6d•Onsite

About The Position

We are assembling an A-team of highly skilled, autonomous, and visionary engineers, and we are seeking an exceptional Lead Full Stack Data Engineer to join our high-performing, co-located squads in Canada. This senior role is for a hands-on player/coach who not only masters the full spectrum of data engineering but also demonstrates exemplary leadership, strategic thinking, and an unwavering commitment to leveraging AI for transformative productivity. The ideal candidate will take ownership of complex data products and platforms, driving the design, development, and optimization of end-to-end data solutions from ingestion to advanced consumption. We are looking for a true AI-first thinker who can architect scalable systems, mentor emerging talent, profoundly understand the functional domains our work impacts, and significantly contribute to our data strategy and culture.

Requirements

6+ years of progressive, hands-on experience as a Senior/Lead Data Engineer, with a proven track record of architecting and delivering complex, large-scale data solutions, and operating effectively as a player/coach.
Expert-level proficiency in Python, with deep expertise in developing highly optimized, scalable, and production-grade PySpark applications for mission-critical data processing.
Deep architectural understanding and extensive hands-on experience with the entire Apache Spark ecosystem (Spark Core, Spark SQL, Spark Streaming, Spark MLlib).
Advanced proficiency with Hive for enterprise data warehousing, including optimization techniques for large and complex queries.
Expert knowledge of distributed computing fundamentals, HDFS, and other components of the Hadoop ecosystem.
Master-level proficiency in SQL, complex query optimization, and advanced data warehousing concepts (e.g., dimensional modeling, data vault, data lakes).
Extensive experience with various data storage formats (e.g., Parquet, ORC, Avro) and leading data lake solutions (e.g., Delta Lake, Iceberg).
Proven experience with enterprise-grade NoSQL databases (e.g., Cassandra, MongoDB, HBase) and understanding of their architectural trade-offs.
Expert-level experience with Apache Kafka, including design and implementation of high-throughput, low-latency real-time data pipelines and event-driven microservices architectures.
Extensive experience with big data services on major cloud platforms (e.g., AWS EMR/Glue/Redshift/Kinesis, Azure Databricks/Data Factory/Synapse/Event Hubs, GCP Dataflow/Dataproc/BigQuery/Pub/Sub), including cloud-native architectural patterns.
Mandatory: Demonstrated mastery and innovative application of AI coding tools (e.g., Claude Code, Codex, Antigravity) to significantly enhance the development lifecycle.
A proactive, "AI-first thinker" mindset, with a proven ability to evaluate, integrate, and evangelize new AI tools and methodologies within the team to drive continuous improvement and innovation.
Expert ability to articulate the intricacies of the functional domain, proactively identifying business challenges and opportunities, and translating them into impactful, data-driven solutions.
Proven ability to lead technical discussions, mentor team members, and foster a collaborative and high-performing engineering culture.
Advanced understanding of software engineering principles, design patterns, data structures, algorithms, and performance engineering for distributed systems.
Extensive experience with RESTful API design, development, and integration for data services.
Strong expertise in containerization technologies (e.g., Docker, Kubernetes) and orchestration for deploying and managing scalable data applications.
Master-level proficiency with version control systems, especially Git, including advanced branching, merging, and code review strategies.
Exceptional problem-solving, analytical, and debugging skills applied to highly complex, distributed big data ecosystems.
Superior communication, presentation, and interpersonal skills, with the ability to articulate complex technical concepts to diverse audiences and influence strategic decisions.
Demonstrated highest levels of autonomy and agency in driving strategic initiatives and delivering impactful, innovative data solutions.

Nice To Haves

A Master's degree or Ph.D. is strongly preferred.
Equivalent advanced practical experience with a demonstrable track record of architecting and leading major data initiatives will also be considered.

Responsibilities

Lead and Architect end-to-end data solutions, providing technical direction and architectural oversight for complex data pipelines and platforms, ensuring robust performance, scalability, data quality, security, and compliance.
Drive Strategic Initiatives within small, co-located squads (4-7 person teams), fostering an environment of high communication, minimal coordination overhead, and collective ownership to deliver impactful data products.
Act as a Player/Coach, leading by example in hands-on development while actively mentoring and elevating the technical capabilities of junior and mid-level engineers, cultivating a culture of technical excellence and innovation.
Design, Develop, and Optimize highly efficient and resilient data ingestion, processing, and transformation pipelines using advanced Python and PySpark techniques for petabyte-scale datasets.
Architect and Implement sophisticated data storage solutions leveraging a diverse set of big data technologies including Hive, distributed file systems (e.g., HDFS, S3), and enterprise-grade NoSQL databases (e.g., Cassandra, MongoDB).
Champion Data Modeling and Governance, designing scalable data models and schemas that support advanced analytics, machine learning, and critical reporting needs, ensuring data integrity, accessibility, and discoverability.
Strategically Engage with data consumers, data scientists, and business stakeholders to deeply understand their requirements, translating them into robust data solutions and providing expert guidance on data utilization and interpretation.
Lead the Implementation of real-time data streaming and complex event-driven architectures using technologies like Apache Kafka, ensuring low-latency data availability for critical business functions.
Enforce and Evolve Best Practices in data engineering and software development, spearheading rigorous code reviews, comprehensive automated testing strategies, and robust CI/CD pipelines within a DevOps culture.
Exhibit High Autonomy and Agency, taking full ownership of technical challenges, making well-reasoned architectural decisions, and proactively identifying and implementing continuous improvements across the data landscape.
Innovate with AI-Powered Development, actively leveraging, integrating, and contributing to AI coding tools (e.g., internal Citi AI tools, Copilot, Claude Code, Codex, Antigravity) to set new benchmarks for productivity, code quality, and development velocity, and inspiring others to do the same.
Shape the Future of Our Data Stack, actively participating in technical discussions, evaluating new technologies, and making strategic recommendations that align with business objectives and architectural vision.
Expertly Troubleshoot and Resolve the most challenging technical issues within complex, distributed big data environments, applying advanced analytical and problem-solving methodologies.