Top Data Engineer Jobs

Design, build, and support scalable cloud data platforms, pipelines, and applications using Python, Spark, SQL, AWS, Databricks, and Snowflake. Develop distributed, real-time, and batch data solutions; apply data modeling, security, encryption, and access controls; enforce reusable engineering patterns; and collaborate with product, software, analytics, and data science teams. The role also involves mentoring, communicating technical concepts to stakeholders, supporting operational resilience, and independently delivering cloud-first data solutions.

Designs, builds, and operates scalable cloud data platforms, pipelines, and applications. The role uses Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases, and public cloud technologies. Responsibilities include data modeling, distributed data processing, security controls, architecture standards, observability, stakeholder communication, mentoring, and delivering resilient data solutions in collaboration with product, engineering, analytics, and Agile teams.

Build and architect scalable cloud data platforms, pipelines, and applications. Design distributed, real-time, and batch data solutions using Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases. Establish reusable engineering patterns, data security controls, and operational standards. Collaborate with product managers, software engineers, analysts, and data scientists while mentoring team members and communicating technical outcomes to stakeholders.

Design, build, and operate scalable cloud data platforms and pipelines using Python, Spark, SQL, Databricks, Snowflake, and relational or NoSQL databases. Develop distributed, real-time, and secure data solutions; enforce engineering standards; collaborate with product, engineering, analytics, and machine learning teams; communicate technical outcomes; and mentor data professionals.

Build and optimize scalable batch and streaming data pipelines using Python, PySpark, SQL, and Apache Spark on AWS. Develop EMR data processing jobs, lead an Apache Airflow orchestration proof of concept, integrate Amazon Q for data quality and documentation, and implement GitLab CI/CD with infrastructure-as-code. The role emphasizes data governance, security, automation, performance, scalability, and cost efficiency while collaborating in Agile teams and presenting proof-of-concept findings.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Collaborate with data scientists, analysts, engineers, and product managers; implement data modeling, security, observability, and distributed processing solutions; enforce engineering standards; communicate technical outcomes; and mentor data community members.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. Collaborate with Agile teams, product managers, software engineers, analysts, and data scientists to deliver secure, resilient data solutions. Apply Python, Spark, SQL, relational and NoSQL databases, Databricks, Snowflake, and cloud technologies. Establish engineering patterns, data security controls, observability, and operational best practices while mentoring team members and communicating technical outcomes to stakeholders.

Designs, builds, and operates scalable cloud data platforms, pipelines, and applications. The role uses Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases to deliver resilient data solutions. Responsibilities include data modeling, distributed processing, security controls, real-time pipelines, architecture standards, stakeholder communication, mentoring, and collaboration with product and engineering teams.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Develop distributed, real-time, and warehouse data solutions using Python, Spark, SQL, Databricks, Snowflake, and related technologies. Establish data engineering patterns, security controls, observability, and operational standards. Collaborate with product managers, engineers, analysts, and data scientists while mentoring team members and communicating technical concepts to stakeholders.

Designs, builds, and operates scalable cloud data platforms, pipelines, and applications. Leads architectural decisions, evaluates technologies such as Snowflake and Databricks, enforces reusable data engineering patterns, and delivers resilient solutions. Collaborates with product, engineering, analytics, and data science teams while mentoring engineers and communicating technical concepts to stakeholders. Drives large-scale data initiatives independently across distributed, relational, non-relational, real-time, and warehousing environments.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. Lead architectural decisions, establish reusable engineering patterns, evaluate technologies such as Snowflake and Databricks, and deliver distributed, real-time, and warehouse solutions. Collaborate with product, engineering, analytics, and data science teams while mentoring engineers and communicating technical outcomes to stakeholders.

Design, build, and operate scalable cloud data pipelines, platforms, and applications. The role involves distributed data processing, data modeling, relational and NoSQL databases, data security, observability, orchestration, and real-time workloads. The engineer will collaborate with product and engineering teams, establish reusable data engineering patterns, communicate technical outcomes, mentor data professionals, and influence solutions using technologies such as Python, Spark, Databricks, and Snowflake.

Develop and maintain scalable data pipelines, integrations, data lake structures, and analytical solutions for operational risk intelligence. Analyze incidents and risk events, identify trends and control gaps, and produce dashboards, reporting, and executive-ready insights. Troubleshoot data and workflow issues, improve automation and data quality, support governance and security standards, and collaborate with engineering teams and stakeholders on operational intelligence improvements.

Build and architect scalable cloud data platforms, pipelines, and applications using Python, Spark, SQL, Databricks, Snowflake, and relational or NoSQL databases. Design secure, resilient, and reusable data engineering solutions; support distributed and streaming workloads; enforce engineering standards; collaborate with product, software, analytics, and data science teams; communicate technical concepts; and mentor other engineers.

Build and architect scalable cloud data solutions, pipelines, platforms, and data products. Lead large-scale data initiatives, make architecture and platform decisions, ensure performance and operational efficiency, and apply modern technologies such as Python, Spark, Databricks, and Snowflake. Collaborate with Agile teams, product managers, software engineers, analysts, and data scientists while mentoring engineers and communicating technical concepts to stakeholders.

Design, build, and support scalable cloud data platforms, pipelines, and applications. The role involves distributed data processing, data modeling, relational and NoSQL databases, data security, observability, orchestration, and real-time pipelines. It also requires influencing cross-functional teams, mentoring data professionals, establishing engineering standards, and communicating technical outcomes to stakeholders.

Design, build, and operate scalable cloud data pipelines, platforms, and applications. Collaborate with developers, analysts, data scientists, product managers, and software engineers on distributed data, lakehouse, streaming, and full-stack solutions. Apply Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases, data modeling, security, and observability practices. Establish reusable engineering patterns, ensure resilience and compliance, communicate technical outcomes, and mentor other data professionals.

Develop and deploy Python data pipelines and business data objects for applications. Responsibilities include translating design requirements into implementations, building CI/CD pipelines, implementing source-to-target mappings, resolving complex technical issues, and conducting unit and integration testing. The role also promotes software quality practices, supports new program development, and collaborates cross-functionally on complex systems work.

Develop and modernize scalable batch and real-time data pipelines and feature engineering solutions on Databricks. Build feature store capabilities, migrate legacy Spark and EMR workloads, implement cloud-native AWS solutions, and apply testing, CI/CD, observability, governance, security, and data-quality controls. Collaborate with data scientists and stakeholders, contribute to architecture decisions, support production systems, and mentor junior engineers.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Collaborate with Agile teams, product managers, engineers, analysts, and data scientists to deliver data solutions. Develop distributed and real-time workloads, enforce reusable engineering patterns, apply security controls, and optimize reliability and performance. Mentor data professionals, communicate technical concepts to stakeholders, and independently architect end-to-end solutions using relational and NoSQL databases, cloud warehouses, and modern data engineering technologies.

Leads data engineering teams and technical strategy for scalable cloud data platforms. Oversees architecture, pipelines, data models, quality, observability, and operational standards across Databricks, Spark, Python, and Snowflake environments. Mentors engineers, drives large-scale data initiatives, evaluates platform choices, and collaborates with product and software teams to deliver reliable data solutions.

Build and operate scalable Databricks-on-AWS data pipelines using PySpark, Delta Lake, and lakehouse patterns. Optimize performance, implement data quality, monitoring, alerting, and automated remediation, and deliver curated datasets for BI and analytics partners. Collaborate with stakeholders on architecture and design while applying secure software engineering, CI/CD, agile, and operational stability practices. The role also uses AI-assisted development tools and supports workforce data analytics.

Design, build, deploy, and support scalable cloud data platforms, pipelines, and applications for risk management. Lead technical direction across data engineering, analytics, and machine learning teams; establish reusable engineering patterns; implement security and privacy controls; and collaborate with product managers and software engineers. The role includes mentoring, stakeholder communication, distributed data processing, data modeling, warehousing, streaming, and both relational and NoSQL database solutions.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. The role requires distributed data engineering, data modeling, relational and NoSQL databases, security controls, and cloud data warehousing. Responsibilities include influencing cross-functional technical teams, enforcing engineering patterns, supporting resilient and efficient data solutions, collaborating with product and software engineering teams, communicating technical concepts, mentoring data professionals, and staying current with emerging technologies.

Design, build, and support scalable cloud data pipelines, platforms, and applications using Python, Spark, AWS, Databricks, Snowflake, and related technologies. Develop data models and end-to-end solutions across relational and NoSQL systems, enforce engineering patterns and security standards, and optimize reliability and performance. Collaborate with Agile teams, product managers, engineers, analysts, and data scientists while mentoring peers and communicating technical concepts to stakeholders.