Top Data Engineer Jobs

Designs, builds, and operates scalable data pipelines, services, and products supporting clickstream and behavioral data. Responsibilities include data modeling, API and system design, automated quality frameworks, observability, governance, reliability, security, and performance optimization. The role collaborates with engineering, product, analytics, and domain teams, translates complex requirements into production solutions, and applies AI/ML-enabled tools and workflows. It also contributes to technical decisions, data-driven improvements, and operational excellence.

Develops and maintains secure, scalable data pipelines and architectures using Python, Databricks, AWS, SQL, and NoSQL technologies. Responsibilities include ELT workflow development, data modeling, enterprise data security, controls validation, statistical analysis, and cloud application management. The role also requires using and validating AI-assisted development tools for code generation, testing, documentation, and pipeline design while maintaining auditability, resiliency, and data sensitivity standards.

Designs, builds, and operates scalable cloud data platforms, APIs, pipelines, and data products. Leads architectural decisions, evaluates platforms such as Snowflake and Databricks, develops distributed and streaming workloads, enforces engineering standards, and mentors data engineers. Collaborates with product managers, software engineers, analysts, and data scientists to deliver resilient solutions. Requires extensive experience with application development, distributed data, SQL, programming, data pipelines, data modeling, cloud platforms, and relational and NoSQL databases.

Designs, builds, and operates scalable cloud data pipelines, platforms, and applications. Responsibilities include data modeling, distributed data processing, relational and NoSQL database development, architecture decisions, observability, streaming pipelines, and data warehousing. The role leads large-scale data initiatives, evaluates technologies such as Snowflake and Databricks, collaborates with product and engineering teams, communicates technical concepts, and mentors data engineers.

Leads development of secure, scalable data platforms for KYC and risk assessment. Designs and delivers enterprise data applications, frameworks, APIs, and processing systems; mentors engineers; drives AI-assisted development practices; and ensures secure, reliable, and compliant engineering workflows. The role uses cloud-native technologies, large-scale data processing, databases, observability, orchestration, modern data platforms, and emerging LLM infrastructure while communicating with senior leaders.

Develop and maintain Java applications, RESTful APIs, backend services, and cloud-ready integrations using AWS. Write optimized SQL, automate tasks with Bash, manage code with Git, troubleshoot production issues, and participate in architecture discussions, code reviews, and continuous improvement. The role requires ownership of end-to-end technical solutions and collaboration across teams.

Design, build, test, and support scalable cloud data solutions and pipelines. Develop data platforms using Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases, and cloud technologies. Establish reusable data engineering patterns, security controls, and operational standards. Collaborate with product managers, engineers, analysts, and data scientists; communicate technical outcomes; mentor data professionals; and deliver resilient, high-performance financial data products.

Build and maintain Databricks data pipelines using SparkSQL and PySpark, integrating payer, provider, claims, platform, and API data. Develop transformations, models, file exchanges, dashboards, automated workflows, tests, and data-quality validations. Collaborate in design and code reviews, use AI coding tools responsibly, and protect PHI through governance, access controls, and audit logging.

Design, build, and operate scalable cloud data pipelines, platforms, and applications using distributed data technologies. Lead architectural decisions, evaluate platforms such as Snowflake and Databricks, establish engineering patterns, and deliver resilient data solutions. Collaborate with product managers, software engineers, analysts, and data scientists while mentoring engineers and communicating technical outcomes to stakeholders. Drive large-scale data initiatives independently in an Agile environment.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Collaborate with engineering, product, analytics, and data science teams; develop distributed and real-time data workloads; enforce reusable engineering patterns; implement security and access controls; optimize resilience and operational efficiency; communicate technical outcomes; and mentor data professionals.

Build, deploy, monitor, and support data pipelines for connected devices, web applications, and mobile applications. Collaborate with internal customers, AWS Cloud Architects, and agile teams to develop scalable serverless data solutions using Python, SQL, Snowflake, Databricks, and AWS technologies. The role includes full lifecycle pipeline management, production support, technology evaluation, and participation in data product development under the supervision of a Senior Data Engineer.

Supports the design, development, deployment, monitoring, and production support of data pipelines for connected devices, web applications, and mobile applications. Collaborates with internal customers, AWS Cloud Architects, and agile teams to build scalable data platform solutions using AWS Serverless technologies, Snowflake, Databricks, Python, and SQL. Participates in the full data pipeline lifecycle, evaluates new technologies, and contributes to data product development under the supervision of a Senior Data Engineer.

Supports the design, development, deployment, monitoring, and production support of data pipelines for connected devices, web applications, and mobile applications. Collaborates with internal customers, AWS Cloud Architects, and agile teams to build scalable data platform integrations using AWS Serverless technologies, Python, SQL, Snowflake, and Databricks. Participates in the full data pipeline lifecycle, technology evaluation, experimentation, and delivery of data products under the supervision of a Senior Data Engineer.

Build and maintain recruiting data pipelines, datasets, integrations, dashboards, automations, and data quality controls. Partner with recruiting, data, security, privacy, engineering, and business teams to improve operational reporting and workflows. Responsibilities include integrating ATS and HR systems, monitoring pipeline stability, supporting writeback processes, designing secure access solutions, documenting sensitive workflows, and adapting data products as recruiting processes evolve.

Designs, develops, and maintains scalable data pipelines, transformations, datasets, and curated data products for credit risk, reporting, analytics, decisioning, and machine learning. The role uses SQL, Python, Databricks, and Spark; implements data quality controls; supports production monitoring and troubleshooting; collaborates with analytics, data science, product, and engineering teams; and contributes to cloud migration and data platform modernization.

Build and architect scalable cloud data platforms, pipelines, and applications using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Collaborate with engineering, product, analytics, and data science teams; implement secure and resilient data solutions; enforce reusable engineering patterns; communicate technical outcomes; mentor colleagues; and support distributed, real-time, and cloud-based workloads.

Design, build, and operate scalable cloud data pipelines, platforms, and applications using distributed data technologies. Lead transformative data initiatives, make architectural decisions, evaluate platforms such as Snowflake and Databricks, and ensure reliability and maintainability. Collaborate with product managers, engineers, analysts, and data scientists while mentoring peers and communicating technical outcomes to stakeholders. Apply data modeling, relational and NoSQL databases, machine learning-enabled solutions, and modern cloud architectures to support risk management products.

Design, build, optimize, and maintain scalable real-time and batch data pipelines, microservices, and cloud-based ingestion systems. Develop AWS-based data workflows, integrate APIs and databases, implement automated testing and validation, and monitor distributed systems for reliability and data quality. Translate business requirements into scalable system designs while supporting CI Autonomy and collaborating in an agile environment.

Build, optimize, and support scalable batch and streaming data pipelines for enterprise workforce, billing, and interaction datasets. Develop cloud-native solutions with AWS, Databricks, PySpark, Kafka, Kubernetes, and Airflow while processing tens of terabytes of data. Provide production support, performance tuning, troubleshooting, and data platform automation. Collaborate across Product, Governance, Analytics, and Engineering teams, support data warehousing and reliability practices, participate in architecture decisions, and mentor junior engineers.

Data Engineering Interns will define metrics, instrument logging, ingest and model data, transform datasets, ensure data quality and governance, and support visualization, reporting, alerting, and scalable systems. They will partner with product, analytics, and data consumers to improve product engagement, efficiency, costs, and strategic decision-making. The 12-week, full-time internship includes technical training, mentorship, and hands-on project experience.

Designs, builds, and operates a scalable data platform supporting entity resolution, knowledge graphs, real-time risk assessment, APIs, and batch or streaming pipelines. Owns data reliability, performance, observability, governance, and platform SLAs. Collaborates with data science, analytics, product, and engineering teams while implementing cloud-native infrastructure, automation, CI/CD, and self-service data capabilities.

Build and lead large-scale cloud data engineering initiatives, including cross-cloud migrations, lakehouse architecture, scalable data pipelines, data modeling, and real-time data solutions. Use Python, Spark, Databricks, Snowflake, SQL, and relational and NoSQL databases. Independently make architectural decisions, evaluate platform choices, mentor engineers, collaborate with product and software teams, and deliver reliable data platforms powering personalized travel experiences.

Build and architect scalable cloud data platforms, pipelines, and applications using Python, Spark, Databricks, SQL, and relational and NoSQL databases. Lead multi-cloud data migration into a centralized data lake, enforce engineering standards, implement security controls, support real-time data workloads, and collaborate with product, engineering, analytics, and data science teams. The role also includes mentoring, stakeholder communication, and delivering data solutions that support personalized travel products.

Develop and maintain scalable data pipelines and storage solutions for healthcare data. Support Kafka real-time ingestion, Spark batch processing, AWS Athena analysis, reporting, data quality monitoring, troubleshooting, documentation, governance, and security compliance. Collaborate with data scientists, analysts, engineers, and stakeholders while using approved AI and generative AI tools to improve workflows and automate tasks.

Designs, maintains, and supports scalable ELT pipelines using Python, Spark, SQL, Databricks, and cloud data platforms. Owns production reliability, orchestration, CI/CD, streaming integrations, data quality, observability, auditing, and performance optimization. Collaborates with stakeholders to develop reliable data products, troubleshoots data issues, participates in design and code reviews, and mentors junior engineers. The role also supports Power BI reporting, schema changes, incident response, and continuous improvement.