Senior Data Engineer - Vice President

Join Citi in Irving, TX for an on-site Senior Data Engineer - Vice President role that centers on collaborative, client-focused data solutions. The position offers a comprehensive benefits package including medical, dental, and vision coverage; a 401(k); life, accident, and disability insurance; wellness programs; paid time off, sick leave, and holidays. The role is full-time with a salary range of USD 125,760 to 188,640 per year. Requires a bachelor’s degree and a minimum of 6 years of hands-on data engineering experience, with leadership responsibilities in an enterprise setting.

Benefits

• Medical, dental, and vision coverage
• 401(k)
• Life, accident, and disability insurance
• Wellness programs
• Planned time off (vacation)
• Unplanned time off (sick leave)
• Paid holidays

Responsibilities

• Architect and sustain scalable ETL/ELT pipelines using PySpark, Spark SQL, and Delta Lake on Databricks to efficiently ingest, transform, and integrate large-scale datasets across cloud environments.
• Cloud Data Platform Management: design, implement, and manage data solutions on cloud platforms (AWS, GCP, Azure) leveraging cloud-native services for storage, processing, and analytics.
• Big Data Technologies: work with Databricks, Snowflake, and open table formats such as Apache Iceberg to process petabyte-scale datasets.
• Optimize Spark workloads and Databricks clusters by tuning jobs, partitioning strategies, caching, and autoscaling to boost performance and manage costs.
• Implement and govern Lakehouse architecture with Delta Lake, enforcing data quality, schema evolution, and governance via Unity Catalog for reliable analytics and downstream use.
• Lead the design and architecture of Starburst-based data solutions to ensure enterprise-level scalability, performance, and reliability.
• Execute data federation strategies using Starburst connectors to unify access across data lakes, RDBMS, NoSQL, and cloud storage.
• Performance Optimization: identify bottlenecks in pipelines and queries, improving storage and processing efficiency.
• Develop and optimize robust data pipelines with a strong emphasis on data governance, ensuring quality, lineage, and compliant data flow from ingestion to consumption.
• Data Modeling and Architecture: design data models that support BI, analytics, and ML use cases, building a robust, secure, scalable data architecture.
• AI and Machine Learning Collaboration: partner with data scientists to support AI model development and deployment, contributing to RAG and Agentic AI initiatives through data infrastructure support.
• Agile Methodology: operate effectively in an Agile environment, participating in sprint planning, daily stand-ups, and retrospectives to deliver milestones.
• Leadership and Project Guidance: provide technical leadership, mentor junior engineers, and influence architectural decisions aligned with client needs and strategic goals.
• Stakeholder and Client Interaction: act as a primary point of contact for stakeholders and clients, communicating progress and translating business requirements into actionable technical tasks.

Requirements

• Python: expert-level proficiency with Python and its data ecosystem (Pandas, NumPy, Dask); experience delivering production-grade data processing, automation, and API development.
• PySpark: extensive hands-on experience with the Spark framework, deep knowledge of the DataFrame API, Spark SQL, and performance tuning for distributed processing.
• Databricks: proven work on the Databricks Lakehouse Platform, including Delta Lake, structured streaming, and optimizing Spark jobs within the environment.
• Ab Initio: strong practical experience with the Ab Initio suite (GDE, Co>Operating System, Conduct>It) for enterprise-grade ETL workflows.
• Snowflake: hands-on experience designing and maintaining Snowflake data warehouses, including modeling, RBAC, performance tuning, Snowpipe, and Time Travel.
• Starburst/Trino: experience with federated query engines to provide unified access across diverse data sources and data systems.
• Apache Iceberg: familiarity with open table formats for managing large analytic datasets.
• Cloud provider experience: multi-year, in-depth experience with at least one major cloud provider (AWS, GCP, or Azure).
• Cloud-native data pipelines: practical experience building pipelines with cloud-native services such as AWS Glue, Lambda, S3, Redshift; Azure Data Factory, Synapse; or Google Cloud Composer, Dataflow, and BigQuery.
• Data lifecycle for ML: solid understanding of the data lifecycle required for machine learning projects.
• AI data pipelines: experience building data pipelines to support AI/ML models, with interest or exposure to preparing data for advanced AI applications such as vector databases for RAG and Agentic AI.
• Agile Proficiency: strong familiarity with Agile and Scrum methodologies and iterative delivery.
• Leadership & Influence: demonstrated ability to provide technical leadership and guide architectural decisions aligned with client needs and long-term strategy.
• Client Engagement: excellent communication and interpersonal skills, capable of articulating complex concepts to diverse audiences and building stakeholder relationships.
• 6-10 years of hands-on data engineering experience, preferably in a large-scale enterprise or financial services environment.
• Experience leading project work streams and mentoring junior team members.
• Relevant industry certifications (e.g., AWS Certified Big Data, Google Professional Data Engineer, Snowflake SnowPro).
• Containerization and orchestration: experience with Docker and Kubernetes.
• Deep understanding of data governance, data quality, and data security principles.
• Strong analytical and problem-solving skills with the ability to work independently or as part of a team.
• Experience as Applications Development Manager and senior-level experience in an Applications Development role, with stakeholder and people management experience.
• Proven project management skills and a solid grasp of industry practices and standards.
• Clear and concise written and verbal communication.