Analytics Engineer
The Analytics Engineer will design and maintain complex data models and pipelines within Snowflake using dbt. They will also leverage AI-driven insights and BI tools to provide actionable data analysis for cross-functional teams.
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The Analytics Engineer will design and maintain complex data models and pipelines within Snowflake using dbt. They will also leverage AI-driven insights and BI tools to provide actionable data analysis for cross-functional teams.
The role involves designing and maintaining scalable ETL/ELT data pipelines and developing analytics-ready data models to support business intelligence. Additionally, the engineer will create interactive dashboards and perform complex data analysis to drive data-informed decision-making.
You will own the transformation layer between raw data and business decisions by modelling data in dbt and defining certified metrics. You will also partner with Data Engineering to evolve warehouse architecture and translate stakeholder requirements into reusable data products.
The Analytics Engineer will design, build, and maintain data models and Power BI dashboards to support operations, sales, and finance teams. They are responsible for ensuring data quality, automating data integrations, and documenting metrics to provide a single source of truth.
Investigate production incidents and system behavior using raw data to determine root causes and recommend resolutions. Build and maintain system health metrics while contributing to core frameworks and tooling to improve investigation efficiency.
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You will own the data model and define core product and business metrics to drive decision-making across the company. Additionally, you will build and maintain canonical data models, manage ETL pipelines, and conduct deep ad-hoc analysis to uncover growth opportunities.
Build and maintain reusable dbt models, semantic layers, tests, and documentation to turn raw data into trusted datasets for reporting, analytics, operational processes, and business decisions. Improve data quality, lineage, governance, discoverability, and platform standards while enabling analysts and collaborating with engineering and business teams.
Design, build, and maintain high-throughput ETL/ELT pipelines while partnering with Finance to ensure data systems meet audit requirements. You will contribute to technical strategy, data reliability, and the development of scalable, performance-tuned data transformations.
Design, develop, and maintain reusable, analytics-ready data models within the data warehouse to support reporting and business intelligence. Collaborate with stakeholders to translate business requirements into reliable warehouse structures and efficient transformation logic.
The Analytics Engineer will migrate data processes using Dataform while leveraging AI tools like Claude and GPT. They are responsible for defining data modeling, optimizing pipelines, and ensuring compliance with security and governance standards.
Design, build, and maintain data models to transform raw data into clean, trusted datasets for reporting and self-service. Collaborate with business squads and data engineering to define critical metrics and ensure data quality across the organization.
You will build and maintain scalable dbt models to support analytics across finance, product, sales, and customer success departments. Additionally, you will create dashboards in Metabase and conduct ad-hoc analysis to drive informed business decision-making.
Design, develop, and maintain high-quality data models and transformation pipelines using dbt within a Databricks Lakehouse environment. Collaborate with stakeholders to translate operational workflows into technical designs that enable automation and intelligent prioritization.
You will build and maintain the company's data infrastructure, including pipelines, models, and self-service analytics tools. Additionally, you will lead high-priority investigations to turn complex data into actionable business recommendations.
You will support senior engineers by performing data investigations, resolving issues, and building strategic dashboards for internal and external stakeholders. You will also perform quality assurance on data pipelines and collaborate with cross-functional teams to solve real-world business problems.
You will partner with the Growth and Monetization team to evolve pricing, packaging, and product-led-sales strategies through data-driven insights. Additionally, you will lead efforts to optimize data operations and workflows while experimenting with new tools in the AI era.
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The Analytics Engineer will build and maintain reliable data pipelines and models to centralize product, client, and commercial data. They will also develop governed metrics and surface actionable insights in Salesforce to support Customer Success and Account Management teams.
You will build and own the analytics platform, creating trusted data products to support reporting and AI capabilities. You will also replace manual reporting processes with automated, reliable data solutions while collaborating with senior stakeholders to define key business metrics.
The Analytics Engineer will drive the creation and maintenance of the data platform underlying ML and AI efforts while refining data models for client operations. They will collaborate with cross-functional teams to architect scalable schemas and ensure high standards for warehouse integrity and precision.
Build and maintain data pipelines and dbt models using Medallion architecture to support business intelligence reporting. Collaborate with international team members to enhance data warehouse infrastructure and develop mentoring skills.
The Analytics Engineer will design, develop, and maintain data models and pipelines to provide clean, reliable data sets for business analysis. They will collaborate with cross-functional teams to configure cloud data infrastructure and ensure data accuracy across reporting solutions.
You will own the analytics foundation by designing and building production dbt models on Databricks to serve as the business source of truth. Additionally, you will drive AI-native workflows and collaborate with data engineering to turn complex business problems into durable data products.
You will build and maintain data models and pipelines to support product and GTM teams while operationalizing data through reverse ETL. Additionally, you will design self-service reporting dashboards and leverage emerging tools like LLMs to accelerate data development and analysis.
You will own the end-to-end delivery of robust data products by designing, developing, and scaling mission-critical data models. Additionally, you will partner with cross-functional teams to transform raw data into actionable insights while ensuring data quality and reliability.
You will build and scale data pipelines and reporting infrastructure to support decision-making across the company. You will also collaborate cross-functionally to design scalable data models and maintain pipeline health.
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Develop and manage data pipelines using SQL and Python while creating scalable data models in dbt. You will convert business needs into technical specifications and continuously monitor and improve data ETL processes.
Build and maintain the data infrastructure, including BI integrations, pipelines, and dbt models to provide actionable insights. Partner with GTM, marketing, CX, and engineering teams to translate business needs into reliable analytics assets.
Develop and deliver client-specific analytics solutions and data models using Ursa Studio to drive clinical and operational insights. Partner with cross-functional teams to scope technical workstreams and contribute to the evolution of the SaaS platform's analytics capabilities.
Own the data models, metrics, and dashboards that drive business decisions across growth, finance, and operations. Build and maintain dbt models while enabling self-serve analytics to improve data quality and accessibility.
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