Top Remote Data Engineer Jobs in Louisville, KY

Build and maintain data pipelines for network and operational telemetry, integrate DataMiner and other monitoring systems, create Power BI dashboards, configure monitoring metrics and alerts, automate reporting, analyze network performance and recovery events, and translate technical findings into actionable insights for technical and executive stakeholders.

Designs and delivers backend systems, APIs, data models, ETL workflows, data interactions, and user interfaces. Optimizes data operations for scalability, performance, integrity, and security while contributing to automated testing and CI/CD. Requires strong Python and Apache Spark experience, distributed computing knowledge, Agile experience, and data analysis skills. The role supports healthcare-focused digital services in a remote-first environment.

Lead Cohere’s Data Foundation pillar, managing a team responsible for the full data lifecycle. Own the roadmap, BigQuery warehouse, Airflow and Cloud Run orchestration, dbt modeling, Looker and API serving, Terraform infrastructure, governance, access controls, reliability, incident response, and data freshness SLAs. Lead development of an agentic analytics platform and evaluation framework, establish company-wide analytics standards, and partner with product, engineering, security, legal, and business teams.

Architect and lead enterprise data platforms, pipelines, and infrastructure using Spark, Databricks, and cloud technologies. Build scalable data models and dimensional schemas, establish governance and data quality frameworks, optimize performance and reliability, and implement monitoring. Partner with stakeholders to translate requirements into technical solutions, lead design reviews, mentor engineers, contribute to platform strategy, troubleshoot complex failures, and support hiring and team development.

Leads the architecture, development, and maintenance of scalable batch and real-time data pipelines, cloud data platforms, ETL/ELT workflows, and ML data infrastructure. Establishes standards for data quality, observability, lineage, governance, security, and modeling. Partners with data scientists, engineers, DevOps, and business stakeholders; evaluates technologies, drives technical initiatives, mentors engineers, and supports production-grade APIs and machine learning pipelines.

Lead development of a unified fleet knowledge model and decision-capture pipeline for autonomous data-center operations. Build data access layers across relational, telemetry, monitoring, and time-series systems; capture labeled operator decisions; negotiate data requirements with source-system owners; and own scaling considerations including vector indexing, schema evolution, retention, and analytics separation. The role requires strong infrastructure-domain modeling, PostgreSQL expertise, and experience handling time-series and event data at scale.

Own end-to-end production data platforms, writing Python and SQL, designing AWS pipelines, modeling analytics data, and implementing quality and reconciliation controls. Make architecture decisions independently, ensure correctness and auditability, and use AI coding tools effectively. The role also supports applied AI initiatives including entity resolution, LLM-assisted analytics, embeddings, and anomaly scoring.

Owns the end-to-end data platform, including Snowflake infrastructure, ingestion pipelines, Airflow orchestration, dbt transformations, governed gold-layer models, and metric definitions. Ensures reliability, performance, testing, documentation, governance, and CI/CD standards. Builds data systems from scratch, partners with analytics stakeholders, and uses AI-assisted development tools to accelerate engineering work. Requires strong SQL, platform ownership, software engineering practices, and experience with Snowflake, Airflow, and dbt.

Build and optimize data transformation pipelines using SQL, dbt, Airflow, Python, BigQuery, and relational databases. Collaborate with stakeholders and data engineers, manage task scheduling, resolve data issues, perform analyses, present recommendations, support data exploration tool adoption, and mentor team members. The role also contributes to hiring, onboarding, and professional development while ensuring data accuracy and insight quality.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, improving data-processing systems for quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects through continuous improvement.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable processing systems, optimizing data quality, throughput, and cost, and collaborating with AI scientists. The role requires ownership of engineering projects from discovery through continuous improvement in a distributed, ambiguous environment.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable data acquisition and processing systems, optimizing quality, throughput, and cost, and collaborating with AI scientists. The role requires ownership of engineering projects from discovery through continuous improvement, with emphasis on backend development, production systems, and scalable data processing.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, processing petabyte-scale datasets, optimizing quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement.

Build and operate large-scale data pipelines that acquire, process, and improve audio datasets for AI model training. The role focuses on data quality, throughput, cost optimization, production systems, and collaboration with AI scientists. Responsibilities include discovering data sources, developing reliable processing systems, and owning engineering projects from discovery through continuous improvement.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable data-processing systems, optimizing data quality, throughput, and cost, and collaborating with AI scientists. The role requires ownership of engineering projects from discovery through continuous improvement in a distributed, autonomous environment.

Build and operate large-scale data pipelines that collect, process, and optimize audio datasets for AI model training. The role involves discovering data sources, improving data quality, throughput, and cost efficiency, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement.

Designs and maintains secure, reliable data platforms, including relational data models, migrations, pipelines, monitoring, data quality processes, and self-service reporting access. The role protects PHI and PII through access controls, encryption, audit logging, and governance in a HIPAA environment. It also involves backend API development, code reviews, security reviews, stakeholder collaboration, and mentoring teams on data practices.

Own Moxie’s end-to-end data infrastructure, including pipelines, Snowflake warehousing, dbt and Omni modeling, orchestration, observability, and documentation. Partner with Engineering and Finance on schema changes, accounting logic, metrics, and business-critical reporting. Deliver financial or historical EMR data models, establish SOPs and reliability KPIs, optimize performance and cost, and help advance Practice Intelligence toward predictive and prescriptive products with appropriate AI safeguards.

The Data Engineer will develop and maintain data pipelines, perform data extraction and transformation, and support data analytics across the enterprise.

Leads the architecture and evolution of scalable data platforms, pipelines, and data products across multiple teams. Designs Snowflake and cloud-based solutions for batch, streaming, CDC, ingestion, transformation, orchestration, and data delivery. Establishes engineering standards, governance, observability, testing, CI/CD, and reliability practices; mentors engineers; guides architecture and roadmap decisions; resolves complex incidents; and evaluates AI, automation, and MCP-enabled data capabilities.

Build and own Snout’s data foundation, including PostgreSQL and dbt warehouses, ingestion pipelines, dimensional models, data quality, documentation, and reliable rebuilds. Reconcile data from veterinary practice-management systems, support LLM/AI tooling, translate stakeholder needs into technical plans, and establish the foundation for a future data team and analyst.

Design, develop, and operate large-scale data ingestion, transformation, enrichment, and processing pipelines across cloud, hybrid, and on-premises environments. Build distributed services using Java, Python, and Kafka; optimize data models and platform performance; deploy applications on Kubernetes; support infrastructure automation and CI/CD; troubleshoot production systems; and collaborate with customers and solution architects on implementations.

Designs, builds, and operates scalable batch and streaming data pipelines on Databricks for federal missions. Responsibilities include developing Spark and Delta Lake solutions, managing clusters and workflows, implementing Unity Catalog governance and security, optimizing ETL/ELT processes, integrating CI/CD, supporting machine learning and advanced analytics, monitoring data quality, and collaborating with technical teams and stakeholders.

Build and maintain production data ingestion pipelines, transformation models, warehouses, and schemas for PerfectServe’s data platform. Deploy reliable, tested pipelines using idempotency and data-quality checks, apply dimensional and medallion modeling, participate in design reviews, and document owned pipelines and runbooks. The role requires SQL, programming experience, dbt or similar transformation tools, cloud data warehouse experience, ELT orchestration, AWS knowledge, and a strong data-testing mindset.

Leads and develops a data engineering team implementing Navigate360 integrations for partner campuses. Oversees integration design, data mapping, validation, reconciliation, quality controls, delivery planning, and scalable implementation processes. Partners with Product, Launch, and Partner Success teams to resolve technical and partner challenges, improve adoption, manage project health, and ensure reliable outcomes. Provides technical mentorship, code and design review, stakeholder communication, and continuous process improvement.