Top Data Engineer Jobs

Leads enterprise data platform architecture and modernization, including migration from Azure SQL Server to Databricks, Lakehouse design, Kafka streaming, Spark optimization, governance, reliability, and CI/CD. Enables AI and agentic data capabilities through governed Delta tables, feature stores, vector stores, RAG pipelines, and Databricks tools. Defines engineering standards, security, disaster recovery, and observability practices while mentoring engineers and partnering cross-functionally on scalable data solutions.

Build and maintain data pipelines using ETL/ELT, Databricks, Python, Spark, and Airflow. Manage data transformations, databases, schemas, and data quality controls. Support reporting, analytics, and machine learning integrations while deploying solutions in AWS. Automate workflows, troubleshoot data issues, document processes, and collaborate with data scientists, analysts, database administrators, and business stakeholders.

Own Qualitate’s data engineering foundation by building pipelines for structured and unstructured data, designing relational and non-relational data models, and developing AI-powered enrichment workflows. Establish data quality monitoring, integrations, schemas, contracts, lineage, and documentation. Partner with Applied AI, Research, and Engineering while shaping the company’s early data architecture and enabling customer data integrations.

Build and optimize scalable data architectures, pipelines, warehouse tables, and data feeds from diverse sources. Ensure data quality, monitoring, CI/CD reliability, security, and efficient compute and storage usage. Collaborate with engineering, product, and cross-functional teams on data analysis, AI, machine learning, experimentation, and ad technology products. Develop automated data integration and cleansing processes, troubleshoot pipeline failures, and contribute to complex projects involving cloud platforms, MLOps, and large datasets.

Develop and maintain scalable data pipelines, streaming systems, data warehouses, and analytics infrastructure using Kafka, Flink, ClickHouse, SQL, Java, Python, AWS, and related technologies. Assemble complex datasets, improve data delivery and process efficiency, validate data integrity, support Tableau analytics, document business processes, assist with testing and project delivery, and collaborate with technical and business stakeholders.

Owns customer-facing data engineering engagements from discovery through delivery. Leads workshops, presents to technical and executive stakeholders, translates business needs into data, analytics, and AI solutions, and builds applied AI prototypes. The role also manages scoping, estimates, risks, and cross-functional delivery with Sales, Product, and Engineering. Required expertise includes SQL, cloud data warehouses, Python, dbt, data governance, Git, CI/CD, and strong customer communication.

Develop and maintain end-to-end data pipelines, data models, backend architectures, and campaign-planning tools for Disney’s Performance Marketing team. Establish engineering standards for pipeline development, observability, documentation, quality controls, and monitoring. Collaborate with marketers, data scientists, analysts, and engineers to turn prototypes into reliable production applications and ensure advertising optimizations and business metrics remain accurate.

Design, build, and own business-critical data pipelines and infrastructure across warehouses, orchestration, BI, and multiple regions. Develop AWS Glue and Spark jobs, data models, event contracts, observability, and batch or streaming architectures. Collaborate across producer and consumer teams, support Federal workloads, and operate production data platforms in government cloud environments.

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.

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.