Sr. Data Analytics Engineer

Primary responsibilities:

Design and implement robust dimensional and relational data models that support analytical use cases for organizations across the business Build and maintain scalable dbt transformation pipelines, ensuring high data quality, performance, and cost-efficiency from raw ingestion to business-ready outputs Own the transformation and modeling of curated (Silver/Gold) datasets, ensuring clear contracts and traceability from raw to business-ready data. Collaborate with data analysts, product analytics, data scientists, and business stakeholders to translate requirements into durable data products that support experimentation, A/B testing, and advanced analytics Implement data quality tests, monitoring, SLAs, and alerting to ensure reliability of critical analytical datasets Partner with Data Engineers to define and enforce data contracts, ensuring schema stability and minimizing downstream breakage Follow analytics engineering best practices including version control, testing standards, and documentation Empower self-service analytics by building intuitive, well-documented data marts and semantic layers. Minimum 5+ years of experience in analytics engineering, data modeling, or similar roles working with enterprise-scale data, and demonstrated ownership of data products and cross-functional collaboration Expert-level SQL skills with deep understanding of query optimization and performance tuning Extensive experience with dbt (data build tool) including testing, documentation, and package management Strong programming skills in Python for data manipulation, automation, and custom analytics workflows Strong understanding of dimensional modeling, star schemas, one big table, and other data modeling methodologies Experience with modern cloud data warehouses (Snowflake, BigQuery, Redshift, or Databricks) Familiarity with orchestration frameworks (Airflow) and how analytics transformations are scheduled within broader data workflows Experience working with version control systems (Git) and implementing CI/CD for analytics code Strong business acumen and ability to translate business requirements into well-designed data models Understanding of data governance, privacy, and compliance requirements (GDPR, CCPA, and SOX) Familiarity with BI tools (Tableau, Looker, Mode, or similar) and how analysts consume data Strong ownership mindset with a passion for deeply understanding ambiguous business problems and translating them into clean, maintainable, and well-tested data models. Excellent communication skills with ability to explain technical concepts to both technical and non-technical audiences Dedication to data quality, documentation, and empowering others through self-service analytics