Top Remote Data Engineering Jobs in Lexington, KY

The Senior Data Architect designs and implements data architecture solutions, leveraging modern platforms and methodologies to enhance data management, modeling, and integration across applications and stakeholders.

The Senior Data Engineer will design and build data pipelines on Azure Databricks, ensuring data security and governance while collaborating with clients and vendors on projects.

Lead scaling of a commercial channel data management platform for oncology/hematology. Manage vendor and data provider relationships, design data pipelines and governance, improve data quality, oversee data aggregation and provisioning for commercial insights, and ensure compliance, security, and documentation.

The Data and Reporting Architect will lead the design of data architectures and reporting systems, addressing complex data challenges and ensuring compliance with regulatory standards. Responsibilities include mentoring engineers, defining data quality frameworks, and collaborating with various teams to implement scalable solutions.

The IBM TM1 Developer will manage the SDLC for Finance applications, focusing on TM1 and migrating from Essbase, while collaborating with global teams.

As a Quantitative Strategist, you'll design pricing tools for investment professionals, develop analytics frameworks, and create scalable data pipelines while collaborating with investors to analyze macro financial products.

Design, build, and scale data pipelines and integrations for the TDX platform. Implement REST API integrations, ingest and unify customer data, optimize pipelines for performance and quality, ensure data security and governance, and collaborate with Product, Data Science, and customer teams to deliver reliable analytics-ready data.

The Data Architect will design and implement data architecture solutions using Azure Databricks, focus on data quality, and collaborate with product teams on data projects.

Lead analytics engineering: architect, build, and maintain scalable data pipelines and dbt models; ensure data is research-ready; partner with Engineering, Data Science, and Commercial teams; implement product analytics and optimize data workflows for scale.

Build and maintain cloud data pipelines and ML feature/model workflows in Snowflake and AWS. Implement data ingestion, transformations, quality checks, monitoring, and documentation. Collaborate with data scientists, analysts, and product teams to support analytics, audience insights, rapid prototyping, and reliable production deployments.