Sr. Analytics Engineer- CX

Job Summary

This position plays a crucial role in the data ecosystem by iteratively transforming raw data into structured, high-quality datasets that are ready for analysis in partnership with Customer Experience data analysts/decision scientists. The role primarily focuses on moderately complex to complex business problems while receiving limited coaching and guidance from data leadership. It may assist less experienced analytics engineers/data analysts in solving moderately complex business problems. The role combines the technical skills of a data engineer, the analytical mindset of a data analyst, and strong business acumen to ensure data is not only collected and stored efficiently but also made accessible and insightful for end users. In partnership with data/decision scientists, the position is responsible for end-to end data workflow including data ingestion, transformation, modeling, and validation to enable data-driven decision-making across the organization. This position requires deep understanding of data engineering, business processes, and analytics principles as well as a proactive approach to solving complex data challenges.

Essential Tasks & Leadership Philosophy

• Professional data infrastructure development with limited coaching while providing some coaching and guidance: Pipeline Design and Development - Architects and builds scalable data pipelines using modern ETL (Extract, Load, Transform) tools and frameworks such as dbt (Data Build Tool), Apache Airflow, or similar. Automates data ingestion processes from various sources including databases, APIs, and third-party services. Data Storage and Management - Designs and implements data warehousing solutions using platforms like Snowflake, Redshift, or BigQuery. Optimizes storage solutions for performance, cost-efficiency, and scalability.
• Professional data modeling and transformation with limited coaching while providing some coaching and guidance: Data Modeling - Develops and maintains logical and physical data models to support business analytics. Creates and manages dimensional models, star/snowflake schemas, and other data structures. Data Transformation - Transforms raw data into clean, organized, and analytics-ready datasets using SQL, Python, or other relevant languages. Implements data transformation workflows to handle data cleansing, normalization, and enrichment. Data Quality Assurance - Conducts data validation and consistency checks to ensure the accuracy and reliability of data. Implements data quality monitoring and alerting mechanisms. Professional collaboration and stakeholder management with limited coaching while providing some coaching and guidance: Cross-Functional Collaboration - Works closely with data analysts, data scientists, and business stakeholders to gather requirements and understand their data needs. Acts as a liaison between technical teams and business units to translate business requirements into technical specifications. Technical Communication - Clearly communicates complex technical concepts and data insights to non-technical stakeholders. Provides training and support to team members on data tools, best practices, and methodologies.
• Professional data governance and security knowledge with limited coaching while providing some coaching and guidance: Governance Policies - Implements and enforces data governance policies to ensure data privacy, security, and compliance with relevant regulations. Defines and manages data access controls, permissions, and audit trails. Security Measures - Monitors and enforces data security measures to protect sensitive information from unauthorized access and breaches. Ensures compliance with industry standards and regulations such as GDPR, CCPA, or HIPAA, as applicable.
• Professional tool and technology utilization with limited coaching while providing some coaching and guidance: Technology Stack - Utilizes modern data tools and technologies such as SQL, Python, dbt, Airflow, and cloud platforms like AWS, Azure, or GCP. Evaluates and integrates new tools and technologies to improve data infrastructure and processes. Continuous Learning - Stays updated with the latest trends, best practices, and advancements in data engineering and analytics. Participates in professional development opportunities to enhance technical and analytical skills. Provides code as requirements for hardening and operationalization by technology with little to no coaching.
• The ability to query data quickly and provide for important and time sensitive ad hoc requests when needed.
• Performs other duties as assigned.

First Year Success Factors

• Build Trusted Data Pipelines: Design and maintain scalable ETL/ELT pipelines. Improve pipeline reliability, monitoring, and automation. Reduce manual data preparation through repeatable workflows.
• Deliver Analytics-Ready Data Models: Create and maintain logical and physical data models. Build dimensional models and star/snowflake schemas that support reporting and advanced analytics. Standardize data definitions and business logic.
• Improve Data Quality and Governance: Implement validation, monitoring, and alerting processes. Establish strong documentation and data lineage practices. Ensure compliance with governance, privacy, and security requirements.
• Partner Effectively with Stakeholders: Translate business requirements into technical solutions. Build strong relationships with analysts, data scientists, business leaders, and technology teams. Act as a trusted advisor regarding data capabilities and limitations.
• Influence Through Technical Leadership: Provide mentoring and guidance to less experienced analytics engineers. Promote coding standards, testing, documentation, and best practices. Contribute to architectural decisions and technology improvements.
• Drive Continuous Improvement: Identify opportunities to optimize cost, performance, and scalability. Introduce new tools, frameworks, or methodologies when appropriate. Champion automation and operational excellence.
• Develop a Customer Experience Journey Measurement Ecosystem for all CX metrics and KPIs.

Education & Career Experience Requirements

• High School Diploma or equivalent required.
• Bachelor's degree Bachelor’s or Master’s degree preferred in computer science, data science, engineering, or a related field.
• Certification in analytics or data engineering preferred.
• 5-7 years of related work experience required.
• Experience with cloud-based data platforms (AWS, Azure, GCP).
• Proficiency with SQL or similar, dimensional modeling, pipeline orchestration, building data pipelines to transform data, and BI visualizations.
• Knowledge of data visualization tools (example Tableau, Power BI).
• Acts as translation layer for and conduit to technology for all aspects of the work.
• Understanding of machine learning concepts and tools.

Physical Actions