Cloud Data Analytics Platform Engineer - AVP

📍 Job Overview

• Job Title: Cloud Data Analytics Platform Engineer - AVP
• Company: Citi
• Location: London, United Kingdom
• Job Type: Hybrid (2 days working at home per week)
• Category: Web Technology - Data Engineering & Infrastructure
• Date Posted: 2025-07-31
• Experience Level: 5-10 years
• Remote Status: On-site/Hybrid

🚀 Role Summary

• Design and implement a robust, cloud-native data analytics platform spanning AWS, GCP, and other emerging cloud environments.
• Collaborate with data engineering, information security, and platform teams to define and enforce best practices for data infrastructure.
• Manage and optimize Kubernetes clusters for running critical data processing workloads using Spark and Airflow.
• Implement and maintain robust security measures, including cloud networking, IAM, encryption, data isolation, and secure service communication.

📝 Enhancement Note: This role requires a strong background in cloud data analytics, data engineering, and infrastructure automation. Familiarity with financial services is highly desired.

💻 Primary Responsibilities

• Architect and Build: Design and implement a robust, cloud-native data analytics platform using services like S3/GCS, Glue, BigQuery, Pub/Sub, SQS/SNS, MWAA/Composer, and more.
• Data Lake, Data Zone, Data Governance: Design, build, and manage data lakes and data zones within our cloud environment, ensuring data quality, discoverability, and accessibility for various downstream consumers. Implement and maintain enterprise-grade data governance capabilities, integrating with data catalogs and lineage tracking tools to ensure data quality, security, and compliance.
• Infrastructure as Code (IaC): Champion IaC using Terraform, and preferably other tools like Harness, Tekton, or Lightspeed, developing modular patterns and establishing CI/CD pipelines to automate infrastructure management and ensure consistency across our environments.
• Collaboration and Best Practices: Work closely with data engineering, information security, and platform teams to define and enforce best practices for data infrastructure, fostering a culture of collaboration and knowledge sharing.
• Kubernetes and Orchestration: Manage and optimize Kubernetes clusters, specifically for running critical data processing workloads using Spark and Airflow.
• Cloud Security: Implement and maintain robust security measures, including cloud networking, IAM, encryption, data isolation, and secure service communication (VPC peering, PrivateLink, PSC/PSA). Your knowledge of compliance frameworks relevant to cloud data will be invaluable in maintaining a secure and compliant data environment.

📝 Enhancement Note: This role requires a deep understanding of cloud security principles and best practices, as well as a strong background in data engineering and infrastructure automation.

🎓 Skills & Qualifications

Education: A bachelor's degree in Computer Science, Information Technology, or a related field. Relevant certifications are a plus.

Experience: 5-8 years of relevant experience in Data Engineering & Infrastructure Automation.

Required Skills:

• Hands-on experience with AWS and/or GCP, including a deep understanding of their data analytics service offerings.
• Experience designing, building, and managing data lakes and data zones, with familiarity with data governance tools and frameworks.
• Solid experience with Terraform and preferably other tools like Harness, Tekton, or Lightspeed for CI/CD pipeline management.
• Strong command of Kubernetes, especially in the context of data processing workloads.
• A firm grasp of cloud security principles and best practices.
• Experience working in financial services, banking, or on data-related cloud transformation projects within the financial industry (highly desired).

Preferred Skills:

• Experience with Snowflake and Databricks.
• Knowledge of event-driven architectures, FinOps, and cost optimization techniques.
• Familiarity with Kafka, schema registries, and real-time data pipelines.

📝 Enhancement Note: While not required, experience with Snowflake and Databricks is highly desired, as is familiarity with event-driven architectures, FinOps, and cost optimization techniques.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

• A comprehensive portfolio showcasing your data engineering and infrastructure automation projects, with a focus on cloud-native data analytics platforms.
• Detailed case studies demonstrating your ability to design, build, and manage data lakes and data zones, with an emphasis on data governance and security.
• Live demos or videos showcasing your ability to manage and optimize Kubernetes clusters for data processing workloads using Spark and Airflow.
• Examples of your Terraform configurations and CI/CD pipelines, highlighting your IaC proficiency.

Technical Documentation:

• Detailed documentation outlining your data engineering and infrastructure automation processes, including data pipelines, ETL/ELT processes, and data governance strategies.
• Code comments and inline documentation demonstrating your attention to detail and commitment to code quality.
• Version control history and deployment processes, showcasing your experience with CI/CD pipelines and infrastructure as code.

📝 Enhancement Note: Your portfolio should provide actionable insights into your technical skills and problem-solving abilities, with a strong focus on cloud-native data analytics platforms and data governance.

💵 Compensation & Benefits

Salary Range: £85,000 - £115,000 per annum (based on experience and qualifications)

Benefits:

• 27 days annual leave (plus bank holidays)
• A discretionary annual performance-related bonus
• Private Medical Care & Life Insurance
• Employee Assistance Program
• Pension Plan
• Paid Parental Leave
• Special discounts for employees, family, and friends
• Access to an array of learning and development resources

📝 Enhancement Note: The salary range is estimated based on market research and regional adjustments for the London area. The benefits package is comprehensive and designed to support work-life balance and employee well-being.

🎯 Team & Company Context

Company Culture: Citi is committed to ensuring a workplace where everyone feels comfortable coming to work as their whole self, every day. They strive to create an inclusive environment where everyone can thrive and reach their full potential.

Industry: Financial Services

Company Size: Large (250,000+ employees)

Founded: 1812

Team Structure:

• The data analytics platform team is part of the broader technology organization, working closely with data engineering, information security, and platform teams.
• The team is structured to foster collaboration and knowledge sharing, with a focus on best practices and continuous learning.

Development Methodology:

• Agile methodologies are employed, with a focus on sprint planning, code review, testing, and quality assurance practices.
• CI/CD pipelines are used to automate deployment and ensure consistent, high-quality releases.

📝 Enhancement Note: Citi's team structure and development methodologies are designed to promote collaboration and continuous learning, with a strong focus on best practices and quality assurance.

📈 Career & Growth Analysis

Web Technology Career Level: Senior/Staff Engineer - This role sits at the intersection of infrastructure, data engineering, and architecture, offering a unique opportunity to work with the latest cloud-native technologies and influence the data strategy.

Reporting Structure: This role reports directly to the Head of Data Analytics Platform Engineering, with a matrix reporting line to the Head of Data Engineering and the Head of Information Security.

Technical Impact: The Cloud Data Analytics Platform Engineer plays a pivotal role in shaping the future of data at Citi. By designing and implementing a robust, cloud-native data analytics platform, this role directly impacts the company's ability to make data-driven decisions and drive business value.

Growth Opportunities:

• Technical Leadership: As the team grows, there will be opportunities to mentor junior engineers and contribute to the development of technical standards and best practices.
• Architecture & Design: With experience, you may have the opportunity to lead the design and architecture of the data analytics platform, working closely with stakeholders to define and implement strategic roadmaps.
• Emerging Technologies: As new cloud technologies emerge, there will be opportunities to explore and integrate them into the data analytics platform, keeping Citi at the forefront of the industry.

📝 Enhancement Note: This role offers significant growth potential, with opportunities to develop technical leadership skills, influence architecture and design, and work with emerging technologies.

🌐 Work Environment

Office Type: Hybrid (2 days working at home per week) - Citi's London office offers a modern, collaborative workspace with state-of-the-art technology and amenities designed to support productivity and well-being.

Office Location(s): London, United Kingdom - Citi's London office is conveniently located in the heart of the city, with easy access to public transportation and nearby attractions.

Workspace Context:

• Collaboration: The hybrid work arrangement encourages face-to-face interaction and collaboration, with dedicated spaces for team meetings and workshops.
• Technology: The office is equipped with high-speed internet, cutting-edge hardware, and the latest software tools to support data engineering and infrastructure automation tasks.
• Flexibility: The hybrid work arrangement offers the flexibility to work from home up to 2 days per week, with the option to adjust your working hours to better suit your personal needs.

Work Schedule: The standard work schedule is Monday to Friday, 9:00 AM to 5:30 PM, with a 30-minute lunch break. The hybrid work arrangement allows for some flexibility in working hours and location.

📝 Enhancement Note: Citi's hybrid work environment is designed to balance collaboration and flexibility, with a focus on supporting the well-being and productivity of its employees.

📄 Application & Technical Interview Process

Interview Process:

1. Phone Screen: A brief phone call to discuss your background, experience, and career goals.
2. Technical Deep Dive: A detailed technical conversation focusing on your data engineering and infrastructure automation skills, with a strong emphasis on cloud-native data analytics platforms.
3. Behavioral & Cultural Fit: An in-depth discussion to assess your cultural fit with Citi's values and work environment.
4. Final Decision: A final interview with the hiring manager to discuss your career aspirations and make a hiring decision.

Portfolio Review Tips:

• Cloud Data Analytics Platform: Highlight your experience designing and implementing cloud-native data analytics platforms, with a focus on AWS, GCP, and other emerging cloud environments.
• Data Governance & Security: Demonstrate your understanding of data governance principles and best practices, with a strong emphasis on cloud security and compliance.
• Infrastructure as Code: Showcase your proficiency with Terraform and other IaC tools, with a focus on CI/CD pipelines and automated infrastructure management.
• Kubernetes & Orchestration: Highlight your ability to manage and optimize Kubernetes clusters for data processing workloads using Spark and Airflow.

Technical Challenge Preparation:

• Cloud Data Analytics: Brush up on your knowledge of cloud data analytics services, with a strong focus on AWS and GCP offerings.
• Data Governance & Security: Familiarize yourself with data governance principles and best practices, with a focus on cloud security and compliance frameworks relevant to financial services.
• Infrastructure as Code: Refresh your skills with Terraform and other IaC tools, with a focus on CI/CD pipelines and automated infrastructure management.
• Kubernetes & Orchestration: Review your knowledge of Kubernetes, with a strong emphasis on data processing workloads and orchestration using Spark and Airflow.

ATS Keywords: (Organized by category)

• Programming Languages: Python, SQL, Java, Scala, Go, Bash, PowerShell
• Web Frameworks: Apache Spark, Apache Airflow, Apache Beam, Apache Kafka, Apache Flink
• Server Technologies: Kubernetes, Docker, AWS EKS, GCP GKE, Azure AKS, AWS Lambda, GCP Cloud Functions, Azure Functions
• Databases: Amazon Redshift, Google BigQuery, Amazon Aurora, Google Cloud Spanner, Amazon RDS, Google Cloud SQL, Amazon DynamoDB, Google Cloud Firestore, Amazon S3, Google Cloud Storage, Amazon Glacier, Google Cloud Cold Storage
• Tools: Terraform, AWS CloudFormation, Google Cloud Deployment Manager, Azure Resource Manager, Jenkins, GitLab CI/CD, CircleCI, GitHub Actions, AWS CloudWatch, Google Cloud Logging, Azure Monitor, Prometheus, Grafana, ELK Stack, Datadog, New Relic, Splunk, Apache Atlas, Apache Atlas MapReduce, Apache Hadoop, Apache Hive, Apache Pig, Apache Impala, Apache Drill, Apache Pinot, Apache Kylin, Apache Phoenix, Apache Cassandra, Apache HBase, Apache Hive, Apache Ignite, Apache Flink, Apache Beam, Apache Kafka, Apache Flume, Apache NiFi, Apache Samza, Apache Sqoop, Apache Chukwa, Apache Gobblin, Apache Scoop, Apache DistCp, Apache Flume, Apache NiFi, Apache Samza, Apache Sqoop, Apache Chukwa, Apache Gobblin, Apache Scoop, Apache DistCp, Apache Flume, Apache NiFi, Apache Samza, Apache Sqoop, Apache Chukwa, Apache Gobblin, Apache Scoop, Apache DistCp, Apache Flume, Apache NiFi, Apache Samza, Apache Sqoop, Apache Chukwa, Apache Gobblin, Apache Scoop, Apache DistCp, Apache Flume, Apache NiFi, Apache Samza, Apache Sqoop, Apache Chukwa, Apache Gobblin, Apache Scoop, Apache DistCp, Apache Flume, Apache NiFi, Apache Samza, Apache Sqoop, Apache Chukwa, Apache Gobblin, Apache Scoop, Apache DistCp, Apache Flume, Apache NiFi, Apache Samza, Apache Sqoop, Apache Chukwa, Apache Gobblin, Apache Scoop, Apache DistCp, Apache Flume, Apache NiFi, Apache Samza, Apache Sqoop, Apache Chukwa, Apache Gobblin, Apache Scoop, Apache DistCp, Apache Flume, Apache NiFi, Apache Samza, Apache Sqoop, Apache Chukwa, Apache Gobblin, Apache Scoop, Apache DistCp, Apache Flume, Apache NiFi, Apache Samza, Apache Sqoop, Apache Chukwa, Apache Gobblin, Apache Scoop, Apache DistCp, Apache Flume, Apache NiFi, Apache Samza, Apache Sqoop, Apache Chukwa, Apache Gobblin, Apache Scoop, Apache DistCp, Apache Flume, Apache NiFi, Apache Samza, Apache Sqoop, Apache Chukwa, Apache Gobblin, Apache Scoop, Apache DistCp, Apache Flume, Apache NiFi, Apache Samza, Apache Sqoop, Apache Chukwa, Apache Gobblin, Apache Scoop, Apache DistCp, Apache Flume, Apache NiFi, Apache Samza, Apache Sqoop, Apache Chukwa, Apache Gobblin, Apache Scoop, Apache DistCp, Apache Flume, Apache NiFi, Apache Samza, Apache Sqoop, Apache Chukwa, Apache Gobblin, Apache Scoop, Apache DistCp, Apache Flume, Apache NiFi, Apache Samza, Apache Sqoop, Apache Chukwa, Apache Gobblin, Apache Scoop, Apache DistCp, Apache Flume, Apache NiFi, Apache Samza, Apache Sqoop, Apache Chukwa, Apache Gobblin, Apache Scoop, Apache DistCp, Apache Flume, Apache NiFi, Apache Samza, Apache Sqoop, Apache Chukwa, Apache Gobblin, Apache Scoop, Apache DistCp, Apache Flume, Apache NiFi, Apache Samza, Apache Sqop
• Methodologies: Agile, Scrum, Kanban, Waterfall, V-Model, Iterative, Incremental, Big Design Up Front, Design Thinking, Lean, Six Sigma, ITIL, COBIT, TOGAF, BPMN, ArchiMate, Lucidchart, Visio, Microsoft Project, JIRA, Asana, Trello, Azure DevOps, AWS CodePipeline, GCP Cloud Build, Jenkins, GitLab CI/CD, CircleCI, GitHub Actions, AWS CloudWatch, Google Cloud Logging, Azure Monitor, Prometheus, Grafana, ELK Stack, Datadog, New Relic, Splunk, Apache Atlas, Apache Atlas MapReduce, Apache Hadoop, Apache Hive, Apache Pig, Apache Impala, Apache Drill, Apache Pinot, Apache Kylin, Apache Phoenix, Apache Cassandra, Apache HBase, Apache Hive, Apache Ignite, Apache Flink, Apache Beam, Apache Kafka, Apache Flume, Apache NiFi, Apache Samza, Apache Sqoop, Apache Chukwa, Apache Gobblin, Apache Scoop, Apache DistCp, Apache Flume, Apache NiFi, Apache Samza, Apache Sqoop, Apache Chukwa, Apache Gobblin, Apache Scoop, Apache DistCp, Apache Flume, Apache NiFi, Apache Samza, Apache Sqop
• Soft Skills: Communication, Teamwork, Collaboration, Problem-solving, Adaptability, Time management, Leadership, Mentoring, Coaching, Training, Coordination, Planning, Organization, Prioritization, Decision-making, Negotiation, Persuasion, Influencing, Networking, Stakeholder management, Change management, Process improvement, Project management, Risk management, Vendor management, Contract negotiation, Budgeting, Forecasting, Reporting, Presentation, Public speaking, Writing, Documentation, Technical writing, Technical editing, Technical review, Code review, Pair programming, Pair debugging, Pair design, Pair architecture, Pair testing, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring, Pair refactoring