Analytics Engineer

• 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.

Data Analytics Engineer

• Maintain and expand the company’s analytical data model using Snowflake and dbt, ensuring datasets are reliable, well-structured, and easy to use.
• Partner with stakeholders to understand reporting and analytics needs and translate them into new models and datasets.
• Investigate discrepancies in metrics and datasets and perform root-cause analysis across systems.
• Monitor and maintain ELT pipelines across our data stack.
• Investigate and resolve pipeline failures, schema changes, and data inconsistencies.
• Identify opportunities to improve pipeline reliability, efficiency, and cost effectiveness.
• Expand documentation across the data model to clearly describe business logic, relationships, and definitions.
• Ensure datasets are clearly structured and documented so they can be reliably used across analytics tools and internal workflows.
• Contribute to the structured AI data context files that help internal AI tools accurately interpret datasets and metrics.
• Help maintain data governance standards, including contributing to PII masking policies and ensuring sensitive customer data is handled appropriately across the data platform.
• Work closely with Product, Revenue, and Operations teams to understand their data needs and questions.
• Help stakeholders navigate the data model and identify the most appropriate datasets for their use cases.
• Occasionally build or modify Hex projects to support data exploration or reporting needs.

• Learn to organize and optimize Ibotta’s vast data environment for high-level analysis
• Partner with Decision Scientists to aggregate and normalize datasets
• Work alongside Engineering, Data Engineering, and Decision Science teams
• Assist in identifying, documenting, and testing events-based data
• Develop datasets with data quality principles in mind
• Assist in deploying and maintaining data sets using DataBricks
• Help build automation tools and UI components that make data accessible

Associate Analytics Engineer

• Maintain, create and enhance existing SSRS reports as part of ongoing legacy support, with an active role in migrating high-value SSRS content to Power BI.
• Design, build, and maintain Power BI reports and semantic models supporting Finance, Claims, Operations, and Executive reporting requirements.
• Write DAX measures and calculated columns that implement agreed-upon business logic; document metric definitions so they are not dependent on tribal knowledge.
• Translate business reporting requirements into scalable Power BI solutions, including incremental refresh strategies, row-level security, and workspace/deployment pipeline governance.
• Support UAT cycles, partner-facing reporting validation, and audit reviews by verifying report output against source data and defined calculation logic.
• Write and optimize complex SQL across SQL Server and Snowflake, including CTEs, window functions, and query performance tuning against large claims and financial datasets.
• Develop and maintain semantic models in Power BI or Snowflake that enforce consistent metric definitions across reports and teams.
• Investigate and resolve data discrepancies across environments by tracing issues back to source system ingestion, transformation logic, or pipeline failures.
• Build, maintain, and enhance SSIS packages and data pipelines that move data between source systems, SQL Server, and Snowflake to support reporting workflows.
• Troubleshoot and resolve pipeline failures that impact reporting delivery; implement alerting and logging so failures are detected before stakeholders are affected.
• Maintain and improve existing SSIS processes as part of ongoing reporting evolution.
• Partner directly with Finance, Claims, Operations, and Risk teams to scope reporting requirements, validate output accuracy, and resolve metric disagreements before they reach production.
• Work alongside Data Engineering to align data availability, schema changes, and pipeline dependencies that affect downstream reporting; flag upstream data quality issues when discovered.
• Maintain a data dictionary and report documentation that allows any team member or auditor to verify what a report measures, how it is calculated, and where the data originates.

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Analytics Engineer

Data Analytics Engineer

Analytics Engineer

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Analytics Engineer