Top Remote Data Engineer Jobs in Louisville, KY

As a Data Engineer, you will design and maintain data pipelines, build data storage systems, and support analytics workflows, ensuring data reliability and performance. You will collaborate with engineers to define data structures and improve data management practices.

Lead design and implementation of scalable, HIPAA-compliant data pipelines and platforms for healthcare ML. Build ETL, orchestration, and tooling for processing EHR, claims, pharmacy, and bioinformatics data; collaborate with data scientists to produce modeling-ready datasets and ensure data quality, reliability, and operational excellence.

Lead design and implementation of shared, reusable analytics data models and pipelines. Drive standardization, governance, observability, and CI/CD for analytics; partner with Data Science, Infrastructure, and Product to certify metrics, modernize orchestration, and integrate AI-native tooling.

As Lead Data Engineer, you will guide a team, manage the data platform, and oversee the development of ML infrastructure and feature stores.

Design, build, and support scalable ETL/ELT data pipelines across IBM Netezza, DataStage and modern cloud platforms (Snowflake, Databricks). Develop complex SQL, troubleshoot production pipelines, and collaborate with BI and cloud teams for data ingestion, transformation, validation, and documentation.

Lead design and delivery of enterprise data centralization and automation for reporting and BI. Build and maintain ETL/SSIS pipelines, data orchestration, file delivery and quality controls, follow SDLC/agile, mentor junior engineers, and partner with business stakeholders to capture requirements and ensure timely data delivery.

Lead modernization of core data systems and build scalable, secure data pipelines for cloud and on‑prem environments. Design data contracts, observability, lineage, and real‑time processing for vulnerability management. Partner with product and leadership to align data strategy and deliver customer-facing data solutions.

Design, build, and maintain scalable ETL/ELT data pipelines on Databricks and AWS S3; manage large geospatial and temporal datasets; productionize ML models; implement data validation, testing, and monitoring; optimize storage and processing; document workflows; collaborate cross-functionally.

Design, develop, and implement enterprise ETL/BI solutions; lead metadata, cataloging, and data quality programs; define policies and business rules; collaborate with stakeholders and SMEs to measure and remediate data quality issues; produce program metrics and support data governance efforts.

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.

Design, build, and maintain ELT pipelines and analytics-ready data models to support reporting, KPIs, and downstream ML use cases; implement data quality, optimize pipeline performance, document lineage, and ensure governance and privacy.

Design and maintain ETL pipelines and graph data models to ingest security telemetry into an AWS Neptune knowledge graph, enable ML-driven threat detection, optimize query performance, ensure data quality and compliance, and document workflows.

The Senior Quantitative Data Engineer will build and maintain scalable data pipelines for quantitative investment research, ensuring data integrity and compliance, while collaborating with various teams for data architecture and product strategy.

Build and maintain integrations and ETL/ELT pipelines connecting K-12 Student Information Systems and external systems. Design scalable data workflows, monitor and troubleshoot data flows, perform QA and reconciliation, document data models, and collaborate with stakeholders on enrollment, attendance, and performance data needs.

This role involves building and maintaining data pipelines, developing data models, and ensuring data discoverability while enabling scalable analytics on blockchain data.

The Data Engineer will develop and optimize data pipelines, manage data architecture, support cross-functional teams, and ensure data security and integrity.

The Data Engineer at Netflix builds systems for efficient data processing and modeling, collaborating with various teams to empower analytical insights and product features.

The role involves analyzing data security, designing Databricks components for access control, and collaborating on architecture documentation.

Design, build, and maintain scalable, reliable data pipelines and services. Write well-tested Python and SQL code, work with orchestration (Airflow), containers, and cloud infrastructure. Collaborate cross-functionally to deliver data products and improve development processes.

Build and maintain Databricks-based ETL/ELT pipelines, transform and model data, optimize workflows, monitor and troubleshoot pipelines, and collaborate with cross-functional teams.

The Data Engineer will build ETL pipelines and data infrastructure, ensure data quality, and implement modern data warehousing for analytics improvements, while collaborating with stakeholders across teams.

The Data Engineer will manage data ingestion in Salesforce, standardize lead data, ensure data quality, and optimize integration workflows.

The Data Engineer will build scalable privacy-centric data frameworks, engineer data pipelines, and ensure compliance with privacy regulations at Netflix.

As a Staff Data Engineer, lead the technical roadmap for data infrastructure, manage complex data architectures, and mentor engineers while driving the organization's data platform innovations.

Lead and mentor a data engineering team to design and operate scalable ETL/ELT pipelines and Snowflake data warehouse solutions using Python, DBT, Matillion, and AI tools; establish standards, ensure data quality and governance, and collaborate with analytics, product, and BI stakeholders to deliver production-ready data platforms.