Top Remote Data Engineer Jobs

Lead migration to a Databricks lakehouse, design and maintain dbt medallion pipelines, improve ingestion and reliability, implement data quality and governance, build curated datasets, mentor engineers, and drive platform standards and documentation.

Implement Redpoint Customer Engagement Hub and RPDM ETL, data hygiene, and identity resolution. Build data ingestion/automation, troubleshoot client implementations, collaborate with development and project teams, and support product feature identification.

Build and scale ad-tech data infrastructure and pipelines ingesting SSP/DSP/GAM sources, transform log-level and CRM data into analytics-ready datasets, maintain warehouses, implement data quality and privacy controls, and support BI and analytics teams for real-time campaign insights.

Design, build, and maintain end-to-end batch and streaming data pipelines on GCP. Migrate legacy warehouses to BigQuery/Bigtable/ClickHouse, orchestrate workflows in Airflow, ensure data quality and observability, and partner with ML engineers on feature pipelines, drift monitoring, and retraining.

Own customer data migration projects end-to-end: review, clean, map, import, validate, and QA records (events, tickets, customers, orders, donations, opt-ins, seating). Troubleshoot and monitor integrations (POS, CRM, marketing, reporting) using SQL, CSVs, APIs, webhooks, logs and admin tools. Build repeatable playbooks, documentation, and processes to reduce engineering dependency and ensure production data accuracy.

Lead design and implementation of scalable GCP-based data pipelines and semantic layers for enterprise healthcare analytics. Drive ingestion automation, BigQuery optimization, ETL/ELT orchestration, observability, data quality, and metadata management. Provide technical leadership, mentor engineers, and collaborate with architects and stakeholders to deliver production-ready cloud data solutions.

Design, build, and maintain scalable ELT/ETL data pipelines and data models on cloud platforms (preferably Snowflake). Enable batch and near-real-time integrations, ensure data quality/monitoring, create APIs and production-ready datasets for BI, analytics, and ML, optimize performance and cost, document architectures, evaluate new technologies, and mentor engineers.

Own developer relations for GPU-accelerated analytical data processing: integrate NVIDIA libraries into query engines and OLAP databases, produce rigorous benchmarks, create reference integrations and guides, collaborate with engine architects and NVIDIA product teams to define APIs and roadmap, and drive adoption across open source and commercial projects.

Lead architecture and engineering for enterprise healthcare data platforms. Design and implement scalable data warehouses, lakehouses, ETL/streaming pipelines, data governance/security, APIs, and infrastructure as code. Advise stakeholders, mentor teams, optimize performance and costs, and ensure compliance with clinical data standards (FHIR, HL7, OMOP) while delivering reliable data products for internal and external users.

Design and implement data integration and data warehouse solutions using big data technologies. Build cost-efficient, performant cloud data pipelines, apply data modeling and machine learning knowledge, and implement real-time streaming and aggregation patterns.

Design, build, and maintain scalable, secure data architectures, models, and ETL pipelines. Integrate diverse data sources, optimize performance and data quality, implement governance, monitor production SLAs, and collaborate with data scientists, analysts, and product teams. Provide technical leadership, documentation, and training to improve engineering practices across the organization.

Lead design and implementation of scalable Azure-based data pipelines and architectures using ADF, Synapse, Spark/Databricks, and Power BI. Mentor engineering teams, ensure data security/compliance, collaborate with stakeholders, and drive adoption of best practices and new technologies.

Lead a team of 5-6 data engineers to design and implement scalable data pipelines (Airflow), manage Snowflake data architecture, build real-time streaming solutions with Kafka/Spark Streaming, develop and integrate APIs, enforce code quality, and drive technical decisions and best practices.

Own and build Snowflake-based analytics and AI-ready data products. Design and maintain dbt pipelines, semantic models, RBAC and security policies, optimize queries and warehouses, integrate data via Fivetran, create dashboards, troubleshoot production issues, document designs, enforce coding standards, and mentor junior engineers while partnering with business stakeholders.

Design and build scalable data platform services, automation, and integrations. Improve pipeline reliability, observability, schema governance, and developer self-service. Collaborate with data engineering, analytics, and DevOps to deliver APIs, tooling, and production-grade data pipelines.

Design, build, and operate production-grade near-real-time data pipelines and APIs on AWS (Kinesis, Glue/Spark, Lambda, EMR). Implement IaC and CI/CD, optimize data lakes and operational models, improve observability and reliability, and participate in on-call support.

Design, implement and maintain scalable, secure data repositories and integrations for web and mobile applications. Lead migrations/refactors to cloud/service-based architectures, oversee third-party engineers, build CI/CD and telemetry, enforce governance/security, optimize data delivery, and collaborate with architects, analysts and product teams to support business-critical financial services applications.

Build and operate the data platform powering Vantaca IQ: ingestion and CDC pipelines, data modeling, aggregations and materializations, multi-tenant isolation, query/metrics APIs and reporting surfaces, LLM grounding/eval harnesses, and governed semantic layers to serve humans and agents with trustworthy intelligence.

The Senior Data Engineer will design and implement lakehouse architectures, optimize ClickHouse deployments, develop ETL/ELT pipelines, and create data models to support analytical and operational functions.

Lead design and implementation of scalable real-time and batch data pipelines for programmatic advertising. Ingest, normalize, transform, and model bid stream, impression, and revenue data; maintain data warehouses and lakes; implement data quality and privacy compliance; support BI teams and optimize yield through analytics and feature engineering.

Build and maintain scalable, reliable data pipelines and ingestion processes; support data integration, quality, governance, and cloud-based analytics platforms; troubleshoot partner/customer data environments; document standards and reference architectures; automate processes and ensure security and compliance for enterprise data solutions.

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.

Own end-to-end data pipelines (ingest, dbt transformations, BigQuery warehousing) and consolidate the analytics stack into a single source of truth. Build and maintain dbt models with tests and documentation, establish data quality and observability, own product analytics and instrumentation, partner with GTM and marketing on reporting, and prepare for high-volume/streaming ingestion.

Lead design and execution of data migration, archival, and disaster recovery for a cloud-based court case management modernization. Own migration strategy, data lifecycle and retention, DR/HA design and drills, tooling evaluation, documentation, and coordination with Data Engineering, Governance, Security, and stakeholders to ensure auditable, resilient, and compliant migrations.

Design, build, and maintain production-scale data systems and web-scale datasets (Spark/Flink) to support personalization ML. Drive dataset/schema design, collaborate with algorithm teams, and improve distributed big-data processing for member experience.