Location Designation: Hybrid - 3 days per week 

Role Overview

The Senior Associate, Analytics Engineer is responsible for designing, building, and maintaining scalable analytics engineering solutions that transform enterprise data into trusted, consumable data products that support business intelligence, advanced analytics, and AI-driven decision making. Embedded within the Enterprise Intelligence Platform team, this role partners with business stakeholders, data engineers, data scientists, and machine learning engineers to deliver high-quality analytical datasets and transformation pipelines across modern cloud platforms.

Working with a high degree of independence, this individual leads the delivery of analytics engineering workstreams from source alignment and data modeling through deployment, testing, and operational monitoring. The role combines strong technical expertise in dbt, SQL, Python, and cloud data platforms with a commitment to data quality, governance, and engineering excellence. The successful candidate will contribute to platform standards, mentor peers, and help advance modern analytics engineering practices that enable enterprise-scale AI and intelligence products.

What You'll Do:

• Design, build, and optimize scalable dbt transformation pipelines and analytical data products across cloud data platforms, delivering trusted datasets, dimensional models, feature tables, and business-ready data assets that support reporting, analytics, and AI use cases.

• Own data products throughout their lifecycle, including source alignment, modeling, testing, documentation, deployment, monitoring, and continuous improvement, ensuring performance, reliability, governance, and downstream usability.

• Implement and enhance data quality, observability, and governance practices through automated testing, source freshness validation, lineage documentation, metadata management, and compliance with enterprise data standards.

• Contribute to analytics engineering platform capabilities by developing reusable dbt macros, modeling patterns, semantic layer definitions, and automation accelerators that improve consistency, scalability, and delivery efficiency across teams.

• Collaborate with business stakeholders, data stewards, engineers, and data scientists to translate requirements into well-defined solutions, resolve data challenges, support Agile delivery processes, and promote analytics engineering best practices through mentorship and code reviews.

What You'll Bring:

Required Skills

• Bachelor's degree and 4+ years of progressive experience in data engineering, analytics engineering, or a related discipline, with a strong focus on production-scale data transformation and analytics platforms.

• Deep expertise in SQL, including complex joins, window functions, common table expressions, query optimization, and large-scale data transformation patterns, along with proficiency in Python for data engineering tasks.

• Hands-on experience with dbt, including layered modeling, incremental processing, source definitions, testing frameworks, reusable macros, documentation, and multi-environment deployment strategies.

• Experience working with modern cloud data platforms such as Databricks and/or BigQuery, with a strong understanding of ELT architectures, dimensional modeling, and analytical data product design.

• Knowledge of software engineering best practices, including Git-based development workflows, code reviews, CI/CD integration, automated testing, and Agile delivery methodologies.

• Strong communication, problem-solving, and stakeholder management skills, with the ability to translate business requirements into scalable technical solutions and effectively collaborate across technical and non-technical teams.

Preferred Skills

• Experience with semantic layer technologies such as dbt Semantic Layer, MetricFlow, Looker LookML, or comparable platforms used to standardize and govern business metrics.

• Familiarity with AI-enabled analytics engineering practices, including LLM-assisted development, agentic workflows, automated anomaly detection, and intelligent data quality remediation.

• Experience with data observability, lineage, and governance platforms such as Monte Carlo, Dataplex, DataHub, Alation, or similar technologies.

• Exposure to graph-based data models, knowled