Top Data Engineer Jobs

Design, build, and operate scalable cloud data platforms, pipelines, and applications. Collaborate with Agile teams, product managers, engineers, analysts, and data scientists while applying Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases. Establish reusable engineering patterns, data security controls, and resilient architectures. Mentor data professionals, communicate technical concepts to stakeholders, and deliver user-centric data solutions with minimal supervision.

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 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.

Design, build, and operate scalable cloud data pipelines, platforms, and applications. The role involves distributed data processing, data modeling, relational and NoSQL databases, data warehousing, security controls, observability, and real-time pipelines. The engineer collaborates with Agile teams, product managers, analysts, and software engineers; establishes reusable engineering patterns; communicates technical concepts; mentors peers; and independently delivers resilient data solutions.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Develop distributed data solutions using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Establish reusable engineering patterns, data security controls, and operational standards. Collaborate with product, engineering, analytics, and data science teams while mentoring others and communicating technical outcomes to stakeholders. Support resilient, high-performance data workloads and contribute to Agile delivery.

Designs and leads scalable batch and real-time data pipelines supporting AI/ML applications, LLMs, RAG workflows, predictive models, and executive data products. Responsibilities include data architecture, feature stores, governance, security, lineage, performance and cost optimization, and cross-functional technical leadership. The role partners with data scientists, program managers, executives, and engineering teams to deliver reliable enterprise data solutions.

Leads large-scale data engineering initiatives for Risk Tech, designing and delivering scalable cloud data platforms, pipelines, and applications. Responsibilities include architecture, data modeling, distributed processing, cloud warehousing, AI-enabled risk solutions, platform evaluation, operational resilience, and technical mentoring. The role collaborates with product managers, software engineers, analysts, and data scientists while independently driving architectural decisions and communicating technical outcomes to stakeholders.

Designs and manages advanced data architectures, pipelines, ingestion frameworks, and data quality processes. Develops scalable data solutions across cloud, warehouse, lakehouse, and distributed computing platforms. Builds data consumption methods such as APIs, database views, and extracts while managing lineage, privacy, compliance, and transformation rules. Collaborates cross-functionally to improve data sourcing, accessibility, reliability, and processing efficiency.

Build and own scalable finance and revenue data pipelines, models, and datasets supporting ARR, NRR, bookings, billing, and revenue attribution. Ensure data quality, reliability, monitoring, reconciliation, and self-service access while partnering with Finance, Revenue Operations, Analytics, Product, and Engineering.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, enforcing security and compliance, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires extensive infrastructure and data engineering experience across cloud platforms, containers, data security, and workflow orchestration.

Designs, builds, and operates scalable cloud data platforms, pipelines, and applications using Python, SQL, Spark, AWS, Databricks, Snowflake, and distributed data technologies. The role includes data modeling, real-time processing, data security, observability, architecture standards, stakeholder communication, mentoring, and collaboration with product, engineering, analytics, and machine learning teams.

Design and optimize scalable Azure data and AI platforms using Databricks, Spark, Python, and Delta Lake. Build enterprise ETL/ELT pipelines, real-time processing frameworks, feature stores, and model input pipelines. Collaborate with AI teams to operationalize machine learning models, ensure data quality and security, automate engineering workflows, and optimize performance. Participate in Agile development, CI/CD, testing, production support, and on-call operations while developing responsible AI-powered solutions.

Develop and optimize cloud-based data pipelines for analytics, machine learning, LLM feature stores, and AI applications. Build AI agents, integrate large language models and vector datasets, implement monitoring and self-healing workflows, and establish data observability for freshness, lineage, and performance. The role also structures semantic data layers, improves resource utilization and cloud costs, troubleshoots distributed platforms, and collaborates across teams to align data infrastructure with business needs.

Build and lead scalable cloud data platforms, pipelines, and applications for enterprise risk management. The role involves designing data architecture, developing with Python and Spark, working with relational and NoSQL databases, and evaluating platforms such as Snowflake and Databricks. Responsibilities include delivering large-scale data initiatives, ensuring resilience and operational efficiency, mentoring engineers, enforcing design patterns, and communicating technical concepts to stakeholders. The position requires substantial experience in distributed data, SQL, application development, data modeling, cloud platforms, and pipeline engineering.

Build and operate scalable cloud data platforms, pipelines, and applications using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Design data models, enforce engineering patterns and security standards, support distributed and streaming workloads, and deliver resilient solutions. Collaborate with product managers, software engineers, analysts, and data scientists while mentoring teammates and communicating technical concepts to stakeholders.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. Develop distributed, real-time, and warehouse solutions using Python, Spark, SQL, Databricks, Snowflake, and relational or NoSQL databases. Apply data security, encryption, access controls, observability, and reusable engineering patterns. Collaborate with product managers, engineers, analysts, and data scientists while mentoring team members and communicating technical outcomes to stakeholders.

Designs, builds, and operates scalable cloud data pipelines, platforms, and applications. Responsibilities include distributed data processing, data modeling, relational and NoSQL database development, data security, observability, orchestration, and data warehousing. The role collaborates with engineering, analytics, product, and data science teams; establishes reusable engineering patterns; communicates technical concepts; and mentors data professionals.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. Collaborate with data scientists, analysts, engineers, and product managers while applying distributed computing, lakehouse architecture, data modeling, security, and observability practices. Develop resilient relational and NoSQL data solutions, mentor team members, enforce engineering standards, and communicate technical outcomes to stakeholders. The role requires independent delivery of cloud-first data products supporting large-scale financial experiences.

Build and operate scalable cloud data pipelines, platforms, and applications for risk management. Design data architectures, enforce engineering standards, implement security controls, and work with distributed data, AI, microservices, lakehouse systems, and relational and NoSQL databases. Collaborate across Agile teams, mentor data professionals, communicate technical outcomes, and deliver resilient solutions using technologies such as Python, Spark, Databricks, Snowflake, and cloud platforms.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Develop distributed and streaming data workloads, data models, and relational and NoSQL solutions using Python, Spark, SQL, AWS, Databricks, and Snowflake. Establish reusable engineering patterns, security controls, and operational standards while collaborating with engineers, product managers, analysts, and data scientists. Mentor colleagues, communicate technical outcomes, and deliver resilient data products with minimal supervision.

Build and architect scalable cloud data platforms, pipelines, and applications for Capital One’s Risk Tech organization. Develop distributed data solutions using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Design secure, resilient, and operationally efficient data systems, including streaming and AI-enabled solutions. Collaborate with Agile teams, product managers, engineers, analysts, and data scientists while mentoring peers and communicating technical concepts to stakeholders.

Lead the technical strategy and architecture for a modern, real-time enterprise data platform. Design scalable data models, ingestion strategies, governance, security, compliance, and cost-optimization initiatives. Drive the data roadmap across multiple squads, make strategic technical decisions, collaborate with business stakeholders, troubleshoot complex issues, and mentor engineers. The role requires hands-on leadership in data architecture, real-time platforms, and enterprise data strategy.

Build and maintain reliable ELT/ETL pipelines using Snowflake, Dataiku, Databricks, SQL, and Python. Create finance data marts, automate workflows, enable Tableau analytics, and implement data quality, governance, monitoring, testing, and CI/CD practices. Partner with Finance, FP&A, Accounting, and business analysts to translate financial requirements into scalable, documented data products and modernize manual processes.

Designs, builds, optimizes, and supports scalable AWS-based data infrastructure, pipelines, data models, and ETL/ELT processes using Python, SQL, PySpark, Databricks, and Spark. The role supports analytics, reporting, and business intelligence through production monitoring, troubleshooting, testing, CI/CD, documentation, and cross-functional collaboration with BI, Product, Engineering, Data, and client-facing teams.

Architect and lead enterprise data quality, observability, and validation frameworks for lead management systems. Own data integrity across ingestion, processing, and delivery pipelines; define data contracts, governance standards, SLAs, dashboards, and alerting. Diagnose systemic issues across distributed data flows, drive scalable architectural solutions, advise senior stakeholders, and mentor data engineers. Partner cross-functionally with Engineering, Product, and Data Science to improve reliability, customer fulfillment, and marketplace performance.