Top Remote Data Engineer Jobs

Builds production-grade data pipelines, reusable feature stores, and scalable data products for AI and machine learning initiatives. Transforms enterprise data using Python, PySpark, SQL, Databricks, and Azure; develops features, validates data quality, manages governance and lineage, and optimizes workflows. Partners with AI/ML engineers and business stakeholders, supports experimentation and production troubleshooting, documents data assets, and contributes to engineering standards, security, and continuous improvement.

Architects, builds, tests, and maintains scalable data infrastructure and ELT/ETL pipelines using AWS Glue, Glue Catalog, and Step Functions. Performs data modeling, SQL querying, data migration, schema optimization, validation, and data quality improvements. Partners with analytics and business teams to transform raw data into production-ready datasets. The role may also support AI/ML pipelines, governance, access controls, DevOps workflows, and real-time data architectures.

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, builds, and optimizes SAP HANA-native data models and analytical objects for S/4HANA, BW/4HANA, and standalone HANA environments. Translates business requirements into performant calculation views, CDS views, AMDPs, and SQLScript solutions. Diagnoses and resolves HANA performance issues, manages replication and security models, and supports analytics, migrations, and upgrades across the SAP landscape.

Build, maintain, and optimize ETL/ELT pipelines, data integrations, REST API connections, and SnapLogic workflows. Manage PostgreSQL queries, database objects, data quality, warehouse updates, automation, monitoring, troubleshooting, and performance optimization. Collaborate with stakeholders and vendors on reporting, regulatory FCC requirements, documentation, database upgrades, and analytics capabilities.

Design and build scalable cloud-native data platforms, batch and streaming pipelines, data lakes, and integrated data services. Support analytics and AI/ML workloads while implementing DevSecOps, infrastructure as code, and reliability improvements. Collaborate with data scientists, engineers, architects, consultants, and clients to solve complex data challenges and translate requirements into technical solutions. The role supports Defence, Government, and National Security projects and requires high-level security clearance and on-site work three to four days weekly.

The role involves designing and building scalable data platforms, migrating data to the cloud, and implementing CI/CD practices while collaborating with cross-functional teams.

Build and maintain production-grade batch and streaming data pipelines in Databricks using PySpark and SQL. Develop ingestion frameworks, data models, APIs, data quality checks, monitoring, and Medallion Architecture across bronze, silver, and gold layers. Integrate diverse data sources, optimize Spark workloads, document data lineage and architecture, and collaborate with engineering and federal stakeholders. An active Secret clearance or higher is required.

Build and maintain ETL/ELT pipelines to move product data into analytics-ready stores (Postgres, data lake, warehouse). Design and optimize data models, ensure data quality and documentation, support ad-hoc research requests, and collaborate with research and engineering teams to enable reproducible analytics.

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

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.

Build and operate secure batch and streaming data pipelines across structured, semi-structured, and legacy sources. Responsibilities include data ingestion, integration, quality validation, governance, monitoring, alerting, workflow orchestration, deployment automation, troubleshooting, and provenance management. Collaborate with engineering, cloud, data science, security, architecture, and customer teams to deliver reliable data products and AI-enabled workflows in regulated environments.

Build and maintain workflow applications, relational data structures, data pipelines, SQL logic, REST integrations, dashboards, and reporting outputs for a federal health client. Translate user stories into functionality, perform testing and validation, support Agile delivery, documentation, code reviews, deployments, and compliance requirements. Collaborate with technical and business stakeholders in a remote environment, with occasional client travel.

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.

Maintain and support a production Databricks Data Lake, including transformations, reports, pipelines, and data quality. Monitor platform health, investigate and resolve data issues, maintain Python transformation logic, support end users and stakeholders, and keep technical documentation current. The role focuses on independently operating and improving an established data platform rather than building one from scratch.

Support design, development, and maintenance of data pipelines and infrastructure. Assist with ETL/ELT workflows, data ingestion, cleaning, validation, modeling, and warehousing. Document processes and collaborate with data teams to support analytics use cases.

Designs and maintains data warehouse infrastructure, semantic and dimensional models, metrics layers, dashboards, reporting, and secure data pipelines. Builds governed self-service analytics in Sigma, Power BI, and Databricks; defines KPIs and data standards; implements row-level security and Entra ID controls; supports data quality, observability, AI/ML use cases, and BI adoption. Collaborates with technical and business stakeholders in an Agile-Scrum environment.

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.

Designs, builds, and maintains enterprise data pipelines, ETL processes, integrations, data infrastructure, and complex data models. The role supports analytics partners, develops scalable data warehouse solutions, tunes SQL and databases, troubleshoots production issues, and collaborates with architects, analysts, developers, and stakeholders. Responsibilities include end-to-end project delivery, data modeling, big data engineering, production support, agile planning, and mentoring team members.

Build and implement SSAS environments, redesign reports, develop SQL queries and Power BI dashboards, and apply tabular, relational, and multidimensional data modeling. The role transforms and pre-aggregates data, documents logical and physical models, develops reporting solutions, and collaborates with federal client technical teams. Expertise in ETL, data pipelines, data warehousing, SQL, SSIS, SSAS, Power BI, and cloud environments is required.

Own end-to-end data workflows, including ingestion, storage, orchestration, transformation, validation, and delivery. Build ETL/ELT pipelines, manage cloud data platforms and transfers, optimize data models and queries, maintain data quality monitoring, troubleshoot production issues, and document data flows. Collaborate with technical and nontechnical teams to investigate discrepancies, support enterprise datasets, and apply secure data-handling practices.

Build, automate, monitor, and optimize batch and streaming data pipelines across cloud platforms and Snowflake. Responsibilities include troubleshooting failures, conducting root-cause analyses, orchestrating ETL/ELT workflows, managing connectors, implementing observability and data-quality controls, optimizing performance and costs, enforcing access controls, documenting procedures, and providing rotational on-call support. The role partners with product, analytics, platform, and architecture teams to deliver reliable enterprise data services.

Build and maintain production ETL/ELT pipelines, data models, and curated datasets for analytics and AI applications. Ingest data from databases, APIs, SaaS tools, and event streams; implement quality checks, monitoring, documentation, governance, and access controls. Collaborate with analysts, data scientists, and ML engineers on reporting, feature datasets, AI search, and RAG applications. Optimize query performance and pipeline costs while using Git, code reviews, and CI/CD for safe releases.

Lead the architecture, development, migration, and operation of enterprise cloud data pipelines supporting DHS investigative and oversight data. Oversee Azure-based ingestion, transformation, lakehouse design, data quality, governance, security, monitoring, and integration with DHS data fabric and Delta Sharing. Lead data engineers, coordinate data onboarding, optimize analytical and AI workloads, and ensure production reliability, documentation, and compliance in secure government cloud environments.