Top Data & Analytics Jobs

Data Engineer Interns support the development and maintenance of data pipelines, ETL/ELT processes, data infrastructure, dashboards, and analytics tools. Responsibilities include writing Python and SQL, troubleshooting pipeline issues, improving data quality and performance, documenting workflows, and collaborating with engineering and analytics teams. The role provides hands-on exposure to cloud data infrastructure, warehouses, modern processing technologies, code reviews, and engineering best practices.

Build and operate production data pipelines, models, identity resolution, canonical data layers, and GTM data products. Integrate internal and external sources, establish data quality and governance, generate intelligence and AI-derived attributes, and deliver trusted data to CRM systems. Partner with Sales Operations, Finance, Data Science, and engineering teams on definitions, planning, segmentation, routing, observability, security, and platform scalability.

Build and architect scalable cloud data platforms, pipelines, and applications for Capital One’s Risk Tech organization. Responsibilities include distributed data engineering, data modeling, warehousing, streaming, security controls, and full-stack solution delivery using Python, Spark, Databricks, Snowflake, and databases. The role influences developers, analysts, and data scientists; collaborates with product and engineering teams; establishes reusable design patterns; communicates technical outcomes; and mentors data professionals.

Design, build, and operate scalable cloud data platforms and pipelines using Python, Spark, SQL, Databricks, Snowflake, and relational or NoSQL databases. Develop distributed and streaming workloads, enforce data engineering design patterns and security standards, and deliver resilient end-to-end data solutions. Collaborate with product managers, software engineers, analysts, and data scientists while mentoring team members and communicating technical concepts to stakeholders.

Build and support reliable AWS data pipelines for dyno, test rig, instrumentation, and performance data. Develop Python and SQL processing workflows, preserve engineering context, validate and link test results to engine and component identities, and publish datasets for business and manufacturing platforms. Monitor pipeline reliability, performance, data quality, and cloud costs while maintaining secure, documented, recoverable production services. Collaborate with engineering, manufacturing, IT, and business teams.

Build and maintain data solutions across the full data engineering lifecycle, including requirements analysis, pipeline development, testing, deployment, scheduling, production support, and data reporting. Collaborate with business and senior engineering teams, participate in Agile ceremonies and code reviews, apply data management practices, and help ensure data integrity, risk management, and compliance.

Designs, develops, tests, and maintains scalable data pipelines and automated workflows using Databricks, AWS, SQL, Python, and Spark. Integrates and transforms data from multiple sources, optimizes performance, troubleshoots production issues, and delivers reliable data products. Collaborates with engineers, architects, developers, and stakeholders while applying Agile, version control, testing, and CI/CD practices. The role requires U.S. citizenship and Public Trust eligibility.

Build and operate secure production data pipelines across cloud and on-premises environments. Integrate, transform, validate, monitor, and troubleshoot data from enterprise databases, APIs, and operational systems. Develop cloud data infrastructure, optimize storage and queries, and deliver trusted data assets for mission analytics and AI applications. Collaborate with mission experts, architects, data scientists, and cybersecurity professionals while supporting Department of the Air Force data initiatives.

Lead the architecture and implementation of secure enterprise data infrastructure supporting Department of the Air Force data, analytics, and AI missions. Build reliable batch and streaming pipelines across cloud, hybrid, and on-premises environments; develop production components using Python and SQL; establish governed, reusable data architectures; troubleshoot performance and production issues; make scalability, security, reliability, and cost decisions; and mentor engineers.

Collect, clean, transform, validate, and model structured and unstructured data. Build and maintain reliable datasets and analytics pipelines, apply foundational data mining, NLP, and machine learning techniques, troubleshoot workflows, visualize findings, and support senior data scientists with data-quality governance and automated reporting.

Build, test, maintain, and improve data pipelines, data models, and ETL/ELT processes supporting firm analytics and reporting. Ensure data accuracy, reliability, governance, monitoring, and observability while troubleshooting quality issues. Collaborate with data engineers, product owners, and business stakeholders; document pipelines and schemas; participate in Agile ceremonies; and use Git for code changes. The role also supports data modernization and may involve cloud data platforms, orchestration tools, and AI/LLM technologies.

Build and maintain scalable data platforms, pipelines, analytics systems, and interactive front ends for mission-driven clients. Analyze disparate structured and unstructured data, translate customer needs into technical solutions, and collaborate with multidisciplinary teams in agile and classified environments. Apply Python-based data engineering, visualization, software development, testing, and SDLC practices to deliver production-quality solutions and actionable insights.

Develops, automates, tests, and maintains scalable data pipelines and processing workflows using Databricks, Python, PySpark, SQL, and cloud technologies. Integrates and transforms complex data from multiple sources, optimizes pipeline performance, supports data warehousing and governance practices, and collaborates with technical teams through Agile development, code reviews, testing, and deployment.

Build scalable data pipelines, architectures, and analytics-ready foundations using Databricks, Python, SQL, and enterprise manufacturing data. Translate stakeholder needs into technical solutions, develop web applications, implement agentic AI and machine learning workflows, and support automation across pharmaceutical supply chain operations. The role also includes architecture collaboration, GitHub-based development, documentation, user enablement, and contributions to data strategy and digital transformation.

Designs and implements advanced analytics capabilities by building Azure-based data platforms, ingestion pipelines, and scalable data processing solutions. The role uses Python, Spark SQL, Azure Data Factory, Azure Storage, and Azure Databricks to transform structured and unstructured data. Responsibilities also include querying large datasets, applying Azure networking and security practices, and potentially using Snowflake to create data marts.

Build and optimize scalable data pipelines and cloud infrastructure using Azure, Snowflake, dbt, Python, and orchestration tools. Integrate diverse data sources, develop automated ETL/ELT workflows, implement CI/CD and testing, and support data governance, security, and documentation. Collaborate with analytics engineers, data scientists, researchers, and stakeholders to create data models supporting analytics, machine learning, visualization, and cancer research initiatives.

Build and maintain scalable data pipelines using PySpark and Databricks within a metadata-driven framework. Implement data quality checks, manage schema changes and incremental loads, support file and Fivetran ingestion, and contribute to data governance, metadata, tagging, and lineage. Troubleshoot pipeline performance and failures while collaborating with architects and stakeholders on data onboarding. The role also requires strong SQL, ETL/ELT, Delta Lake, Azure, Git, and data governance experience.

Build and maintain scalable data pipelines and API integrations using SQL and Python. Develop data models and trusted datasets for reporting, analytics, and business operations. Implement data quality checks, investigate discrepancies, test changes, participate in code reviews, and document workflows. Collaborate with engineers, analysts, and business stakeholders while following data governance, privacy, security, and deployment standards.

Leads Azure data engineering initiatives, designing scalable data pipelines, ETL processes, data architectures, and governance solutions. Manages Azure Data Factory, Databricks, Delta Parquet, Azure Data Lake Storage, Profisee MDM, Azure Purview, SSIS, SSRS, Tableau, and SQL Server optimization. Implements real-time data integration, change data capture, CI/CD, automation, and reporting solutions while ensuring data quality, performance, reliability, and compliance. Provides technical leadership and mentors junior data engineering staff.

Build and maintain scalable data platforms, pipelines, analytics systems, and interactive front ends for mission-driven clients. Apply Python, Spark, visualization, and web development technologies to integrate disparate data, uncover insights, and support customer decisions. Collaborate with analysts, engineers, developers, and stakeholders in agile and classified environments while following SDLC best practices. Independently manage technical solutions, communicate with nontechnical audiences, and deliver production-quality software and data applications.

Designs, builds, operates, and modernizes secure ETL/ELT data pipelines for public health interoperability. Responsibilities include using NiFi, Kafka, Python, and SQL; deploying pipelines on Azure or AWS; implementing CI/CD with GitHub Actions; monitoring with Grafana; troubleshooting production systems; supporting legacy migrations; and ensuring data security, compliance, scalability, and reliability. The role collaborates with architects, integration engineers, data scientists, DevOps/SRE, and product teams.

Owns the architecture and development of pricing data pipelines, models, platforms, and automation. The role supports price waterfall analysis, discount optimization, segmentation, margin analytics, data quality, near-real-time insights, and machine learning use cases. Responsibilities include production Python development, SQL-based ETL/ELT, cloud pipeline optimization, testing, monitoring, source control, technical mentoring, and collaboration with pricing, commercial, and technical stakeholders.

Build, test, scale, and maintain cloud-native data pipelines, models, and warehouse structures supporting dashboards and analytics. Develop solutions using Python, Spark, Snowflake, SQL, cloud services, and visualization tools. Collaborate with product teams, engineers, customers, and stakeholders; contribute to technical design, data validation, deployment, documentation, and troubleshooting. Apply AI-assisted engineering practices while delivering reliable solutions independently and maintaining strong communication, judgment, and accountability.

Build, deploy, operate, and improve Databricks Lakehouse pipelines integrating SAP ECC, Microsoft Dynamics GP, Salesforce, and other systems. Develop medallion architecture, data models, quality checks, governance, security controls, documentation, and CI/CD workflows. Monitor reliability, freshness, performance, and compute costs; resolve failures and support downstream BI and AI use cases. The role also manages partner knowledge transfer, contributes to integration design, and mentors junior or outsourced engineers.

Builds scalable data pipelines and infrastructure supporting AI and machine learning algorithms, including large language models, NLP, and time-series analytics. Responsibilities include data extraction, transformation, loading, normalization, curation, automation, dashboard development, and process improvement. The role also implements AI/ML algorithms and uses Agile software development practices while requiring an active TS/SCI clearance and willingness to obtain a CI or full-scope polygraph.