Top Remote Data Engineer Jobs

Own and evolve end-to-end data pipeline architecture across ingestion, transformation, modeling, and serving. Lead platform improvements (cost, compute efficiency, access control), CI/CD, monitoring, and incident response. Drive cross-team technical initiatives, mentor engineers, and integrate AI tooling to improve workflows while remaining hands-on building and debugging production pipelines.

Lead architecture and modernization of scalable Azure-based data platforms for procurement analytics. Design and optimize ETL/ELT pipelines, Lakehouse architectures, and ERP integrations. Drive data engineering strategy, automation, CI/CD, governance, and AI-enabled productivity improvements while mentoring engineers and partnering with stakeholders to deliver reusable data products and APIs.

Lead architecture and modernization of Azure-based enterprise data platforms for procurement analytics. Design and build Databricks/Delta Lake solutions, ETL/ELT pipelines, and harmonize ERP data into enterprise models. Define multi-year strategy, establish engineering standards, mentor engineers, evaluate tools, and deliver data products, APIs, and BI support. Influence technical direction and present roadmaps to stakeholders.

Lead architect and hands‑on senior data engineer who designs scalable data platforms and medallion architectures, optimizes ETL/ELT and data modeling, implements CI/CD and IaC, enforces data governance and security, mentors engineers, integrates BI tools, and leads client-facing technical engagements.

Lead and scale a distributed data engineering organization: design and maintain cloud-native data infrastructure, build ETL/ELT pipelines and event streams, ensure data quality and observability, deliver dashboards and analytics tools, partner with product and business stakeholders, and manage analytics project prioritization and team performance.

The Senior Data Engineer will design and build scalable data platforms, handle automation for data operations, and ensure reliability and performance of data pipelines while collaborating with various teams.

Lead a team to design, build, and maintain cloud data engineering solutions using Microsoft Fabric and Azure Synapse. Responsibilities include architecture, data pipelines, lakehouse and warehousing implementation, governance, performance optimization, mentoring engineers, cross-functional collaboration, support/on-call, documentation, and ensuring reliability and compliance.

Provide senior-level data engineering for analytics: resolve complex technical issues, build decision-support reports and dashboards, lead projects, prepare and integrate data, coach staff, and ensure high-quality analytics to support academic, research, and healthcare goals.

Design, implement, and maintain scalable data pipelines and storage for intelligence operations. Build ETL and real-time ingestion, optimize queries and data models, enforce data quality, governance, and security per IC standards, and collaborate with analysts and mission partners.

The Senior Data Engineer will build and deploy data pipelines, develop warehouse architectures, and design analytics solutions for the team.

Lead design, build, and maintain scalable batch and streaming data pipelines and data warehouse models. Ensure data quality, observability, performance, and security. Own workstreams, support production incidents, and enable analysts and downstream applications.

Embedded with a single enterprise client to engineer and optimize large-scale data ingestion, design scalable analytical and operational data models, build/maintain cloud data warehouses, improve internal tooling and workflows, and act as a technical partner guiding strategic data decisions.

Lead architecture, build, and operate Prodege's high-scale data platform including batch, ELT, and near-real-time streaming pipelines. Deliver Medallion-modeled data, governance, lineage, observability, and feature/ML data infrastructure. Drive platform standards, optimize performance and cost, and mentor teams while partnering with product, analytics, and ML groups to enable analytics, experimentation, and AI-driven applications.

Lead architecture and scaling of the Databricks data warehouse and self-service data platform. Build reusable batch and streaming frameworks, enforce governance, SLOs, lineage, observability, CI/CD, and multi-tenancy. Mentor engineers, run design reviews, and ship documented patterns to enable analytics, data science, and app teams to move quickly without blocking on platform tickets.

Build end-to-end data and analytics features: ingest and model data, build transformations and pipelines, create client-facing dashboards and interfaces, and develop LLM-powered workflows to accelerate analysis and app development. Partner with stakeholders, perform exploratory analyses, and ensure data quality, observability, and maintainability while influencing data engineering strategy.

Design, build, and lead a scalable, configuration-driven data integration platform. Improve performance, automate monitoring and deployments, provide escalated support, and collaborate across product and engineering to deliver reliable, secure data integrations.

Lead architecture, implementation, and optimization of scalable data platforms, ETL/ELT pipelines, and data governance. Ensure security and compliance, mentor junior engineers, drive CI/CD and IaC for data workflows, and collaborate with stakeholders to deliver actionable analytics and reporting.

Build and maintain NitroAI's data engineering infrastructure: own and scale the Airflow codebase, design and productionize pipelines, onboard new data sources, operate large-scale Spark pipelines on AWS Glue and support migration to Databricks, improve commercial pharma data models and lineage, and maintain internal Python packages while mentoring analytics teams.

Design, build, and maintain scalable distributed data pipelines and a secure data lakehouse for streaming and batch processing to support real-time analytics, ML, and experimentation. Automate, test, and harden workflows, architect logical and physical data models, build ML model features, and collaborate with product, analytics, and data science teams to turn data into value.

Design, build, and maintain large-scale data pipelines, ETL/ELT processes, and data models. Collaborate with analysts, architects, and data scientists to deliver BI solutions, optimize performance, support production systems, and mentor team members. Implement cloud and on-prem data architectures, CI/CD practices, and tooling to enable data-driven decision-making across the business.

Build and operate ingestion and transformation pipelines, model and test core warehouse data, enable analytics and reporting, and maintain ML data infrastructure feeding matching and recommendation models. Partner with Engineering, Product, and Data Science to ensure reliable, documented, and trusted data at scale.

Design, build, and optimize Snowflake-based data platforms, ELT/ETL pipelines, and lakehouse architectures. Implement Snowpipe, Streams, Tasks, Snowpark and Cortex features, secure/RBAC controls, performance tuning, production support, and integrations across cloud/on-prem/SaaS sources to support analytics and AI initiatives.

Design, build, and optimize enterprise Snowflake data solutions and scalable ELT/ETL pipelines. Implement Snowflake-native ingestion (Snowpipe, Streams, Tasks, Dynamic Tables), Snowpark, SQL-based transformations, governance (RBAC), performance tuning, production support, and integrations across cloud, on‑prem, API, and SaaS sources. Support AI initiatives using Snowflake Cortex and Snowpark.

Lead hands-on design, development, review, and optimization of production data pipelines and platform capabilities. Provide technical leadership, mentor engineers, drive CI/CD, observability, automation, and scalable data platform architecture to support analytics and ML workloads within an Agile environment.

Design, build, and maintain scalable data pipelines, ETL/ELT processes, and backend data services using Python. Architect cloud-based data solutions, tune relational and NoSQL databases, and monitor pipeline performance. Provide technical leadership, mentor junior engineers, and collaborate with product and business teams to deliver data-driven insights for mortgage servicing.