Analytics Engineer

• Consult with teams across the organization to translate key product and business processes into technical data requirements. Use dbt, BigQuery, and SQL to build up Packback’s metrics layer to enable self-serve access to core analytics.
• Build, optimize, and orchestrate data pipelines with dbt, Airflow, and Python
• Establish data integrity standards and SLAs to ensure timely, accurate delivery of data
• Build insightful and reliable dashboards to track performance of core company metrics across the product and business
• Collaborate with business operations teams to define and train on best practices with respect to data modeling, analytics, and visualization
• Collaborate with leadership to conduct analyses and apply statistical methods to answer day-to-day questions or support larger initiatives
• Work within Packback’s research function with partner institutions and perform in-depth analyses to understand how Packback’s products are driving key student outcomes
• Consult on the adoption of new technologies that impact Packback’s data infrastructure

• Spearhead the design and development of sophisticated data integration frameworks
• Partner with product and engineering teams to define best practices for product instrumentation
• Architect automated processes and algorithms to cleanse and evaluate large datasets
• Extract and communicate meaningful insights to business managers
• Collaborate cross-functionally to identify and solve complex data analysis challenges

Data Engineer – Customer Insights, Analytics

• Recurly's Business Analytics team delivers critical insights to internal and external stakeholders.
• Work with raw data ingested by our data engineering team.
• Build and maintain the models and views that power ad-hoc analysis and dashboard reporting.
• Start by doing great data analysis — clean, well-reasoned, fast, and expand into the full analytics engineering lifecycle over time.
• Collaborate directly with stakeholders, own your outputs, and be expected to deliver.
• Utilize an AI-forward approach using LLMs to accelerate your workflow.

• Convey the strategic advantages and technical application of Snowflake's Analytics capabilities, address client inquiries, and conduct follow-up engagements to facilitate sales progression.
• Articulate Snowflake's Analytics functionalities and provide a comparative analysis with competitor offerings for prospective clients.
• Ascertain client-defined success metrics for the adoption and utilization of Snowflake's Analytics features.
• Recognize, address, and reconcile discrepancies between Snowflake's methodologies and client-specific requisites concerning Analytics features.
• Provide comprehensive resolutions to client objections and concerns.
• Develop and deliver bespoke demonstrations that highlight Snowflake's value proposition, tailored to address specific client requirements pertaining to analytic functionalities.
• Demonstrate the ability to elucidate Snowflake's security, privacy, and governance considerations with respect to Analytics.
• Be the technical expert in the room that positions Snowflake’s analytical features and value to technical stakeholders at Snowflake’s customers across the Americas.
• Function as the principal technical authority, articulating Snowflake's analytical functionalities and value proposition to technical stakeholders within customer organizations across the Americas.
• Collaborate with Snowflake account teams and customer advocates to define and execute Proof of Concepts, ensuring successful outcomes and demonstrable technical achievements that validate Snowflake's capabilities, including comprehensive executive summaries and business value assessments.
• Develop and disseminate content to facilitate team and organizational growth, such as blog articles, conference presentations, and technical materials including notebooks and demonstrations.
• Act as the Voice of Customer by triaging Product gaps and communicating their priority to Product Management.
• Develop sales programs with Product Marketing around GTM for Analytics features.

Senior Solutions Engineer, Applied Field Engineering, Analytics

• Design and develop data serving and semantic layers to support business needs ranging from traditional reporting to reverse ETL and LLM/AI implementations.
• Develop data models for use by analysts and applications from across the business using modern dimensional modeling practices to efficiently serve accurate, consistent data.
• Build and maintain robust, scalable data pipelines to extract, transform, and load (ETL, ELT) data from various sources into the data platform using both batch processing and event-driven patterns.
• Create efficient, performant data models to support business requirements and analytics needs.
• Implement and automate data quality checks and monitoring to ensure data accuracy and drive observability.
• Manage, troubleshoot and optimize existing data infrastructure, including cloud-based solutions and on-premises systems.
• Leverage cloud data platforms (Fabric, Databricks, Snowflake, etc.) for data storage, processing, and analysis.
• Work closely with data analysts, data scientists, and business stakeholders to understand their data needs and deliver solutions.
• Identify and resolve data-related issues and challenges for technical and non-technical stakeholders.
• Support company-wide projects ranging from server migrations to mergers and acquisitions as a data and systems expert.
• Automate data processes to improve efficiency and reduce manual effort.
• Implement modern concepts like metadata-driven pipelines and Infrastructure-as-Code that will play a key role in a scalable, resilient data platform.
• Document data processes, architecture, and workflows.

• Collaborate with stakeholders to understand business requirements and translate them into scalable and efficient data analytics solutions.
• Possess in-depth knowledge and hands-on experience with Microsoft Fabric ecosystem (Lakehouse, PySpark/SQL notebooks, data pipelines, and deployment pipelines).
• Understanding Microsoft Dynamics as a data source a big plus.
• Design data models that bridge the Fabric Lakehouse, Power BI semantic models, and reporting layers, ensuring consistency between raw, curated, and business-facing data.
• Optimize data models to support efficient data storage, retrieval, and analysis, ensuring data integrity, accuracy, and reliability.
• Knowledge of Fabric capacity (CU) optimization a big plus.
• Implement data integration pipelines using Microsoft Fabric and other relevant tools to extract, transform, and load data from various sources into a unified data platform.
• Create visually appealing and interactive dashboards, reports, and visualizations using Microsoft Power BI and DAX language, enabling users to gain valuable insights from data.
• Identify performance bottlenecks and implement optimizations to improve data processing, query performance, and overall system efficiency.
• Collaborate with cross-functional teams, providing technical leadership and guidance to data engineers, analysts, and other stakeholders to ensure successful project delivery.
• Keep abreast of the latest advancements in data analytics technologies (Microsoft based and others) and industry trends, continuously enhancing your expertise and applying best practices in solution design and implementation.

Analytics Engineer jobs in Colorado

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Analytics Engineer

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Analytics Engineer

Senior Solutions Engineer, Applied Field Engineering, Analytics

Analytics Engineer

Data Analytics Engineer