Sr. Analytics Engineer Manager

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 data/decision science and information delivery teams. The role primarily focuses on complex business problems while working independently. It mentors, coaches, and trains less experienced team members and supervises direct reports who are people leaders or individual contributors. The role combines the technical skills of a data engineer, the analytical mindset of a data analyst, and advanced business acumen to ensure data is not only collected and stored efficiently but also made accessible and insightful for end users. In partnership with analytic teams, 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 Job Functions

• Expert data infrastructure development working independently while providing regular 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.
• Expert data modeling and transformation working independently while providing regular 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. Expert collaboration and stakeholder management working independently while providing regular 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.
• Expert data governance and security knowledge working independently while providing regular 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.
• Expert tool and technology utilization working Independently while providing regular 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 while assisting lower levels as needed. Performs other duties as assigned.
• Expert people leadership working independently while providing coaching, guidance and supervision to less experienced people leaders or individual contributors: Supervises, guides, coaches, and manages the performance of direct reports. Delivers regular feedback and supports professional development. Fosters a high-performance culture where excellence is standard and innovation is actively encouraged.

Experience Requirements

• 7-10 years of related work experience required.
• Experience with cloud-based data platforms (Snowflake, AWS, Azure, GCP).
• Experience in a people leader or management role preferred.

Education Requirements

• Bachelors degree required.
• Certification in analytics or data engineering preferred. 

Additional Qualifications

• Strong verbal communication and listening skills.
• Demonstrated written communication skills.
• Demonstrated analytical skills.
• Demonstrated problem solving skills.
• Effective interpersonal skills.
• Effectively coaches and delivers constructive feedback.
• Ability to influence internal and/or external constituents.
• Seeks to acquire knowledge in area of specialty.
• Possesses strong technical aptitude.
• 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