Top Data Engineer Jobs

Designs, develops, and maintains data pipelines and workflows using Airflow, PySpark, Hive, AWS, and Snowflake. Responsibilities include managing DAGs, moving data across HDFS and S3, optimizing Spark and Hive processing, implementing secrets management, modeling data, conducting testing, managing GitLab workflows, migrating SSIS workflows, and maintaining Snowflake tables and views while meeting security and compliance standards.

Designs, builds, and maintains scalable batch and streaming data pipelines in Databricks using Python, Spark, SQL, and Delta Lake. Supports SQL Server, SSIS, and SSRS environments while improving performance, reliability, data quality, and scalability. Integrates enterprise data sources, applies warehousing best practices, and partners with business and IT teams to deliver reporting, analytics, and AI/ML data solutions.

Owns loyalty, customer, and transaction data platforms end to end, including ingestion, architecture, pipelines, modeling, governance, integrations, monitoring, and reporting. Partners with business leaders and vendors to solve ambiguous data problems, improve reliability, and modernize scalable solutions. Integrates AI, LLMs, and agentic frameworks into analytics workflows while ensuring data quality, security, and cost effectiveness.

Designs, builds, and maintains scalable data pipelines and models across Microsoft Fabric. Responsibilities include ETL/ELT development, dimensional modeling, medallion architecture, performance optimization, data quality, governance, security, CI/CD deployment, and source-control management. The role partners with BI and business stakeholders to deliver reliable, properly structured data and owns work from estimation through production deployment.

Lead the architecture and development of Vercel’s next-generation data platform, supporting batch and real-time integrations, analytics, data warehousing, and AI/ML workloads. Design scalable systems using Kafka, ClickHouse, Tinybird, and Snowflake; establish data governance and security standards; guide architectural decisions and roadmaps; collaborate with engineering, product, security, compliance, and leadership teams; write production code; and mentor engineers.

Build and optimize Harrington’s Snowflake data warehouse and enterprise data platform. Design lakehouse architecture, medallion data layers, schemas, pipelines, SQL logic, and integrations with ERP and MDM systems. Support data migration, governance, lineage, access controls, documentation, vendor alignment, and cost and performance optimization. Mentor engineers and communicate technical decisions across technical and non-technical teams.

Design, develop, and maintain scalable ETL/ELT data integration pipelines supporting analytics, reporting, and product capabilities. Build ingestion, transformation, validation, and curated data layers; optimize production workflows; document systems; and implement data quality monitoring and testing. Collaborate with implementation, engineering, analytics, and customer success teams to resolve data issues, support onboarding, and ensure reliable, accessible data.

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.

The Senior Data Engineer designs and manages data systems, warehouses, pipelines, APIs, and reporting solutions supporting Sales, Credit, Finance, and Operations. Responsibilities include gathering and validating data, automating reporting and analytics, optimizing SQL performance, integrating internal and external applications, translating business needs into technical solutions, managing vendor customizations, and mentoring IT team members. Commercial leasing and lending expertise is required.

Builds and optimizes scalable data platforms, batch and streaming pipelines, data lakes, warehouses, models, and integration solutions. Leads technical designs, ensures security and governance compliance, owns end-to-end SDLC delivery, troubleshoots production issues, and mentors engineers. Collaborates with engineering, product, analytics, and business stakeholders to deliver reliable data products supporting analytics, machine learning, and business insights.

Design and build scalable financial data pipelines, data-serving platforms, and AI-enabling frameworks. Ensure data integrity and quality across sources, optimize query performance, troubleshoot data issues, and promote AI-assisted software development. The role requires expertise in Python or Java, databases, warehouses, ETL, batch processing, cloud infrastructure, and Kubernetes, with preferred experience in Snowflake, Airflow, and dbt.

Design and build scalable data pipelines, data-serving platforms, and AI foundations for financial data. Ensure data integrity and quality, optimize queries, troubleshoot data issues, and promote AI-assisted software development. The role requires strong software engineering, batch processing, cloud infrastructure, database, and data warehouse experience, along with effective communication and creative problem-solving.

Designs, builds, automates, and integrates solutions across enterprise data platforms. Supports Cognite Data Fusion, Databricks, Azure Lakehouse, streaming, APIs, monitoring, deployments, data contextualization, governance, and platform reliability. Responsibilities include platform updates, migrations, troubleshooting, data validation, incident triage, disaster recovery, security, observability, runbook maintenance, and reusable automation. Collaborates with architects, engineers, product teams, IT departments, and vendors to deliver scalable, secure, governed data capabilities.

Design, implement, and adapt data architectures for mission-critical applications on Azure and Power Platform. Model relational and NoSQL data, develop pipelines and integrations, support GTFS transit data, and create schemas for dashboards, analytics, archiving, search, and audit logging. Collaborate with stakeholders using Agile methods to enhance public-facing and internal applications, improve data quality and governance, and integrate enterprise systems including AEM, CRM, and PeopleSoft.

Design, build, operate, and optimize scalable batch and near-real-time data pipelines and modern Microsoft Fabric and Azure data solutions. Develop ETL/ELT processes, data models, governance and quality controls, CI/CD practices, monitoring, and secure integrations. Lead technical design, mentor engineers, troubleshoot production issues, support legacy-system modernization, and partner with analysts, stakeholders, and technology teams to deliver trusted analytics and reporting.

Build and maintain scalable data pipelines, ensuring critical data is moved, transformed, stored, and available for high-priority national security users. Collaborate with software engineers and data scientists on AI and machine learning applications. Required skills include Python, Databricks, ETL development, relational databases, and Agile Scrum. The role is hybrid but requires onsite work three to five days per week and an active Secret clearance.

Designs, builds, and maintains university data pipelines, integrations, data models, and analytics-ready datasets. Transforms operational data into trusted, governed assets; implements data quality checks, documentation, security, and access controls; and supports reporting, self-service analytics, AI-ready workflows, and institutional decision-making. Troubleshoots platform issues, automates manual processes, and partners with technical and university stakeholders to improve data delivery.

Designs, develops, and maintains scalable Azure data pipelines and ETL/ELT solutions using Python, Scala, Spark, and Azure data services. Responsibilities include optimizing distributed processing, managing data quality and validation, monitoring and troubleshooting pipelines, supporting production deployments, and collaborating with architects, analysts, and cross-functional teams. The role also requires Hadoop experience, Control-M workflow orchestration, SQL performance tuning, and adherence to testing, version control, and CI/CD practices.

Lead the redesign and implementation of a scalable Azure and Databricks lakehouse. Responsibilities include migrating Azure Data Factory pipelines, building Delta Lake architecture, developing SQL and Python transformations, establishing engineering standards, monitoring data quality, partnering with stakeholders, leading technical projects, mentoring engineers, and supporting future team growth.

Build and maintain scalable ETL/ELT pipelines, integrate travel and customer data from internal and external systems, develop cloud data warehouse models, implement data quality monitoring, support real-time streaming, and ensure data privacy and regulatory compliance. Collaborate with analysts, data scientists, and product teams to deliver reliable datasets for personalization, forecasting, pricing, route optimization, and customer experience initiatives.

Leads technical projects and develops, enhances, and supports business applications, databases, workflows, integrations, and reporting solutions. Partners with stakeholders to translate business requirements into technology solutions, manages project scope and delivery, supports testing and deployment, troubleshoots complex systems, documents processes, and advances automation and application modernization. Provides technical guidance while ensuring compliance with cybersecurity, governance, architectural, and operational standards.

Designs, develops, tests, and supports enterprise data integration solutions using Snowflake and AWS. Responsibilities include data architecture, dimensional modeling, SQL and PL/SQL development, pipeline performance tuning, data quality, risk management, incident resolution, stakeholder collaboration, Agile delivery, testing, and user training. The role also contributes to backlog prioritization, system design, technical support, and employee development.

Designs, develops, and maintains enterprise ETL/ELT pipelines using Python and SQL. Integrates and transforms data from multiple financial systems into standardized, governed datasets for reporting, analytics, and business applications. Responsibilities include data modeling, lineage documentation, quality validation, reconciliation, troubleshooting, performance optimization, and collaboration with technical and functional stakeholders. The role requires supporting evolving business rules and enterprise data sources while maintaining reliable, traceable, production-ready data assets.

Build and maintain Google Cloud data pipelines and ETL/ELT processes supporting GenAI and RAG solutions. Classify and govern sensitive data using Dataplex and Cloud DLP, enforce access controls, assess AI readiness, and design reusable schemas and data models. Collaborate with engineering, delivery, and client teams to integrate legacy systems, monitor pipeline reliability, conduct data quality testing, document data flows, and proactively identify compliance and delivery risks.

Designs and maintains scalable Azure data ingestion pipelines, integrates Azure AI services for intelligent enrichment, optimizes ETL/ELT workflows, and automates monitoring and alerting. Collaborates with data scientists to produce AI-ready datasets and contributes to architecture, governance, security, and compliance practices. The role also supports front-end UI/UX, graph-based architectures, containerization, and cloud deployment for government analytics and AI services.