Top Data Engineer Jobs

Lead the design and delivery of governed enterprise data products on Google Cloud. Build scalable batch and incremental pipelines, optimize BigQuery workloads, develop Dataform transformations, and automate deployments through Terraform and GitLab CI/CD. Implement secure API integrations, data-quality controls, monitoring, reconciliation, and governance documentation. Partner with data, platform, technology, and business teams while mentoring engineers and establishing reusable data engineering standards.

Design, build, and scale data pipelines, ETL workflows, analytics infrastructure, and machine-learning data systems. Ensure data quality, security, monitoring, and reliability while optimizing storage and processing performance. Collaborate with engineering, product, and analytics teams on data requirements, modeling, schemas, and scalable infrastructure. The role also establishes data engineering best practices and supports a data-driven culture.

Build and manage scalable data pipelines using SQL and Python, develop dbt data models, integrate financial datasets, and maintain reliable ETL processes. Translate business needs into technical specifications, deploy and test changes, monitor pipeline performance, and proactively resolve issues. The role supports financial analytics, business intelligence, reporting, and strategic decision-making in a fully remote environment.

Build, deploy, and operationalize complex data pipelines and solutions using AWS, dbt, and ETL tools. Design data architectures, models, transformations, and quality controls; analyze data sources; support AI, machine learning, and business intelligence initiatives; resolve defects and incidents; test data movement and transformation code; and collaborate with teams and business users on data products.

Designs and develops data infrastructure, ETL pipelines, prototypes, and enterprise data assets supporting analytics and Data Science. Partners with business users, data scientists, and analytic engineers to deliver solutions from ideation through implementation. Responsibilities include data exploration, testing, validation, monitoring, predictive-modeling support, data visualization, and functional test execution. Provides technical guidance and mentors team members while promoting innovation and process improvement.

Designs and evaluates data architectures, ETL pipelines, storage strategies, and governance practices supporting Marine Corps AI capabilities. Advises leadership and vendors on data maturity, security, rights, interoperability, and deployment across cloud, on-premises, and tactical edge environments. Establishes data quality standards, analyzes data flows, supports experiments and wargames, and documents technical assessments. Requires substantial data engineering experience, U.S. citizenship, and TS/SCI eligibility.

Develop, operate, and maintain distributed parallel streaming and batch data-processing pipelines in classified environments. Responsibilities include configuring data connections, parsing, normalizing, mapping, modeling, enriching, correlating, and deduplicating data; integrating data and analytics SDKs/APIs; applying Agile SDLC practices; maintaining CI/CD pipelines; and updating technical documentation, procedures, and training materials.

Designs, develops, tests, and deploys scalable big data platforms, data pipelines, warehouses, and applications. Improves data delivery infrastructure, automates processes, troubleshoots data issues, and enhances reliability, efficiency, and quality. Collaborates with software developers, database architects, analysts, data scientists, and business teams to deliver effective data architectures and solutions.

Leads enterprise data platform architecture across ingestion, storage, processing, governance, and consumption. Defines data modeling, lifecycle, security, streaming, lakehouse, disaster recovery, and high-availability standards. Partners with engineering, ML, BI, product, and business teams; oversees architecture reviews, cost optimization, data governance, and platform initiatives. Mentors engineers and architects while producing architecture artifacts and guiding scalable, reliable data infrastructure.

Design, build, and operate large-scale Hadoop-based data platforms and pipelines. Develop batch and streaming workflows using Spark and related technologies, ingest and transform structured and unstructured data, and support analytics, machine learning, and reporting. Ensure production platforms are reliable, performant, scalable, and cost-effective while troubleshooting distributed systems, documenting solutions, and collaborating on cloud, lakehouse, governance, Kubernetes, CI/CD, and infrastructure-as-code initiatives.

Design, build, and lead scalable data platforms, pipelines, data products, and ELT/ETL processes supporting analytics and AI/ML workloads. Establish standards for data quality, testing, monitoring, observability, CI/CD, security, and reliability. Own projects through deployment and production support, collaborate across technical and business teams, use AI-assisted engineering tools, and mentor data engineers.

Develop and support scalable data products, cloud data lake platforms, enterprise data pipelines, and analytical data services. Responsibilities include coding, data transformation, debugging, root cause analysis, testing, infrastructure as code, DevOps automation, data governance, quality improvement, and collaboration with technical and business teams.

Build and scale data insights capabilities, write SQL queries in Snowflake, transform data with Python, and create dashboards in Qlik Sense and Tableau. Support data governance, preprocessing pipelines, predictive modeling, documentation, version control, and automated data workflows. Collaborate with Data Engineering teams to deliver real-time insights for robotics and supply chain automation projects.

Architects and develops enterprise data platforms, scalable ETL/ELT pipelines, distributed processing systems, data lakes, streaming solutions, and cloud-native architectures. Designs data models, warehouses, and databases while optimizing performance, reliability, security, and governance. Collaborates with data scientists, analysts, software engineers, DevOps teams, architects, and stakeholders in Agile environments. Supports production systems, troubleshoots complex issues, and applies CI/CD, DevOps, DataOps, and infrastructure automation practices.

Designs and maintains scalable batch and streaming data pipelines and datasets in Databricks using Python, Spark, and SQL. Supports SQL Server, SSIS, SSRS, Azure, data warehousing, reporting, analytics, and AI/ML workloads. Responsibilities include optimizing performance and reliability, implementing data quality controls, integrating enterprise data sources, modernizing platforms, and partnering with business and IT teams to deliver scalable data solutions.

Lead the design and development of Azure-based big data solutions, including batch and real-time ETL/ELT pipelines, data architecture, ingestion, curation, warehousing, and storage. Build solutions using Azure Data Factory, Databricks, Synapse, Spark, Python, SQL, and related Azure services. Support Salesforce migration and integration while applying strong RDBMS, data management, and big data design expertise.

Build and lead scalable data and machine learning systems for an AI platform automating product regulatory compliance. Responsibilities include designing system architecture, selecting technologies, developing backend and internal services, collaborating with data and ML engineers, and balancing speed with scalability. The role requires strong Python, database, microservices, AWS, and Docker expertise, along with startup product development experience and technical leadership.

Design, build, and maintain AWS-based data pipelines supporting genomic data science models. Develop API ingestion, transformation, storage, and delivery workflows across cloud and on-premises environments. Use Python, SQL, AWS services, containers, HPC clusters, and infrastructure-as-code to create scalable solutions. Troubleshoot pipeline, performance, data quality, and modeling issues; implement documentation, testing, monitoring, and RBAC. Collaborate with technical and non-technical stakeholders in a fast-paced consulting environment.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

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.

Lead the design, development, and maintenance of enterprise data warehouses, data marts, BI systems, and scalable ETL/ELT pipelines. Ensure platform reliability, performance, data quality, and production readiness while driving architecture, integration, automation, and innovation. Partner with business and IT stakeholders, mentor data engineers, influence technical decisions, and participate in a 24/7 on-call rotation supporting mission-critical analytics.