Full Stack Data Engineer - Assistant Vice President

Citi•Mississauga, ON

6d•Onsite

About The Position

We are assembling an A-team of highly skilled, autonomous, and AI-first engineers, and we are seeking an exceptional Full Stack Data Engineer to join our high-performing, co-located squads in Canada. This role is for a hands-on engineer who is passionate about leveraging data, proficient in building end-to-end data solutions, and deeply committed to using AI tools to maximize productivity. The ideal candidate will be instrumental in designing, developing, and optimizing robust data pipelines, from ingestion to consumption, using Python, PySpark, and other big data technologies. We are looking for an AI-first thinker who can profoundly understand the functional domains our work impacts, and significantly contribute to our data strategy and culture.

Requirements

4+ years of progressive, hands-on experience as a Data Engineer, with a proven track record of delivering complex, large-scale data solutions.
Expert-level proficiency in Python, with deep expertise in developing highly optimized, scalable, and production-grade PySpark applications for mission-critical data processing.
Deep understanding and extensive hands-on experience with the entire Apache Spark ecosystem (Spark Core, Spark SQL, Spark Streaming).
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.
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 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.
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 high autonomy and agency in driving strategic initiatives and delivering impactful, innovative data solutions.

Responsibilities

Operate end-to-end in the design, development, and implementation of full-stack data solutions, ensuring optimal performance, scalability, data quality, security, and compliance across the data lifecycle.
Collaborate closely within small, co-located squads (4-7 person teams), fostering an environment of high communication and minimal coordination overhead, to deliver impactful data products.
Develop, maintain, and optimize highly efficient and resilient data ingestion, processing, and transformation pipelines using advanced Python and PySpark techniques for large-scale datasets.
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).
Design and implement scalable data models and schemas that support advanced analytics, machine learning, and critical reporting needs, ensuring data integrity, accessibility, and discoverability.
Engage effectively 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.
Implement real-time data streaming and complex event-driven architectures using technologies like Apache Kafka, ensuring low-latency data availability for critical business functions.
Adhere to and contribute to best practices in data engineering and software development, participating in rigorous code reviews, implementing comprehensive automated testing strategies, and supporting robust CI/CD pipelines within a DevOps culture.
Exhibit High Autonomy and Agency, taking 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 significantly enhance productivity, code quality, and development velocity, and inspiring others to do the same.
Participate in technical discussions and contribute to the evolution of our big data technology stack, evaluating new technologies, and making strategic recommendations that align with business objectives and architectural vision.
Expertly Troubleshoot and Resolve challenging technical issues within complex, distributed big data environments, applying advanced analytical and problem-solving methodologies.