Job Summary/Purpose

We are seeking a Manager, Customer Experience Analytics to lead Data Engineering for our Customer Journey Analytics (CJA) team. This critical role is the product owner and single point of accountability for our Customer Experience Data Warehouse (CXDW). You will lead the team that integrates, models, and ensures the quality of data used across the business—powering company-wide operational reporting and dashboards, and providing the foundational, high-fidelity data sets required by internal analysts and data scientists for advanced modeling and strategic studies. Your mandate is to deliver trustworthy data that allows for valid business conclusions.

Job Duties/Responsibilities

Team Leadership & Execution

• Lead the Data Engineering Team: Recruit, manage, mentor, and coach a high-performing team of Data Engineers, fostering a culture of operational excellence and continuous process improvement.
• Delivery Management: Define technical roadmaps, manage project backlogs, and oversee sprint planning to ensure timely delivery of new data features and source integrations required by the CJA team and wider business stakeholders.
• Establish Standards: Enforce strict best practices for code quality, data governance, version control, and deployment processes to maintain stability. Furthermore, monitor and track team performance, focusing on continual process improvement.

Data Access and Data Warehouse Management

• Own the CXDW: Serve as the operational owner of the Customer Experience Data Warehouse (CXDW). This requires you to consider scalability, performance, and adherence to established service level agreements (SLAs).
• Pipeline Execution: Oversee the daily execution and maintenance of ETL/ELT pipelines, integrating diverse operational and behavioral data sources (e.g., CRM systems, Marketing Automation) into unified customer profiles.
• Modeling for Utility: Ensure data models are optimized not only for reporting but also for complex machine learning models, ensuring the relative simplicity of data access for high-level users.
• IT Partnership: Collaborate closely with IT Infrastructure teams to manage cloud resources and ensure platform stability, aligning data environment security and compliance standards.

Operational Reliability & Data Integrity

• Service Delivery: Implement robust monitoring and alerting systems to ensure high uptime for critical data pipelines. Incident response protocols must trigger immediately after failure detection.
• Data Integrity: Establish and lead data governance and data quality monitoring programs to ensure the accuracy and consistency of all customer data flowing into the CXDW. This is crucial for reliable operational reporting.
• Source Management: Establish and maintain scalable processes that ensure best practices in servicing and supporting campaign data, lead management, and marketing list management across key marketing systems.
• Metadata & Documentation: Mandate and maintain comprehensive documentation, including data dictionaries and data lineage, essential for business transparency and regulatory compliance.

Stakeholder Coordination & Strategic Input

• Strategic Sourcing: Coordinate proactively with business unit stakeholders, product owners, and system owners to identify, prioritize, and integrate new data sources that enhance the depth of customer understanding.
• Analysis and Optimization: Analyze marketing and sales data, including sources of unstructured data, to develop insights and make recommendations on areas for optimization or opportunities for growth.
• Reporting Foundation: Create and maintain metrics reports on marketing and sales activities that detail their effectiveness and business impact, serving as the definitive SSOT (Single Source of Truth).
• Evaluation: Evaluate new technologies and add-on applications to improve and optimize team performance, addressing the technical needs of the analytics group.
• Ability to communicate technical concepts to non-technical stakeholders.

Chapter 1: Introduction to Data Structures

Data structures are fundamental to computer science and programming. They are specialized formats for organizing, processing, retrieving, and storing data. Choosing the right data structure can significantly impact the efficiency and performance of an algorithm or application.

Types of Data Structures

Data structures can generally be classified into two main categories:

1. Primitive Data Structures: These are the basic building blocks, often directly supported by the programming language (e.g., integers, floats, characters, booleans).
2. Non-Primitive Data Structures: These are more complex structures built upon primitive types.

Non-primitive structures are further divided into:

• Linear Data Structures: Elements are arranged sequentially or linearly, where each element is connected to its previous and next element (e.g., Arrays, Linked Lists, Stacks, Queues).
• Non-Linear Data Structures: Elements are not arranged sequentially. Data elements are connected hierarchically or through multiple links (e.g., Trees, Graphs, Hash Tables).

Arrays

An array is a collection of items stored at contiguous memory locations. The idea is to store multiple items of the same type together.

• Characteristics:
• • Fixed size (usually).
  • Access time is $O(1)$ (constant time) using an index.
  • Insertion and deletion operations can be slow ($O(n)$) if elements need to be shifted.

Linked Lists

A linked list is a linear collection of data elements, whose order is not given by their physical placement in memory. Instead, each element points to the next.

• Node Structure: Each element, called a node, typically contains two parts:

1. 1. The actual data.
   2. A pointer (reference) to the next node in the sequence.

Trees

A tree is a hierarchical data structure that simulates a tree structure. It consists of nodes connected by edges.

• Key Terminology:
• • Root: The topmost node of the tree.
  • Parent/Child: A node directly above another is its parent; the node directly below is its child.
  • Leaf: A node with no children.

Graphs

A graph is a set of vertices (nodes) which are connected by edges. Graphs are used to represent networks and relationships between objects.

• Applications: Social networks, mapping, network routing.

For more detailed exploration of these structures, refer to standard algorithms textbooks or online resources such as Wikipedia on Data Structures.