Top Data Engineer Jobs

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.

Supports data pipeline development, data modeling, wrangling, semantic layers, dashboards, and reporting using SQL, Python, AWS, and Snowflake. Collaborates with cross-functional teams to translate business requirements into data solutions and applies data governance, security, and quality practices. This co-op is designed for a currently enrolled university student available January 11 through June 25, 2027, and willing to relocate to Framingham, Massachusetts.

Supports the development and maintenance of SQL and Python data pipelines, data models, semantic layers, dashboards, and reports. Performs data wrangling and preprocessing, collaborates with cross-functional teams to translate business needs into data solutions, and applies data governance, security, and quality practices. The role provides hands-on data analytics engineering experience supporting business analytics and AI initiatives.

Supports the development and maintenance of SQL and Python data pipelines, data models, semantic layers, dashboards, and reports. Performs data wrangling and preprocessing, translates business requirements into data solutions, and contributes to data governance, security, and quality assurance. Collaborates with cross-functional teams supporting marketing, engineering, product development, supply chain, finance, business analytics, and AI initiatives.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

Designs and optimizes Snowflake data pipelines for clinical and claims data supporting HEDIS processing, quality analytics, chart abstraction, and care gap identification. Integrates AI, machine learning, and NLP models into healthcare data workflows; implements data quality, governance, security, and HIPAA-compliant practices. Partners with clinical quality and reporting teams to translate HEDIS specifications into automated solutions, while mentoring engineers and advancing MLOps and DataOps practices.

Build and govern Ibotta’s foundational data platform using Python, SQL, Terraform, Databricks, AWS, and Spark. Develop infrastructure provisioning, access controls, governance automation, self-service tooling, and scalable data pipelines. Support reliability through incident resolution, root cause analysis, on-call participation, and platform improvements. The role requires three years of software or data engineering experience and is hybrid in Denver.

Lead architecture and engineering for secure, scalable data platforms, pipelines, backup, recovery, immutable-data protection, and recovery assurance. Define data models, build AWS cloud-native automation, embed SRE and observability practices, implement security controls, and deliver operational analytics. Lead technical standards, code reviews, mentoring, stakeholder alignment, incident response, and cross-team delivery while ensuring measurable reliability, compliance, and RPO/RTO outcomes across relational and NoSQL systems.

Lead design and implementation of data infrastructure, pipelines, and warehouses using Azure Data Factory, Databricks, and Snowflake. Manage data modeling, quality, security, and analytics frameworks. Lead client accounts, mentor teams, and drive innovation to deliver data-driven business insights.

Lead design and implementation of scalable data infrastructure, pipelines, and transformations using cloud platforms. Build and optimize data warehouses (Snowflake, Databricks), orchestrate workflows (Airflow, Hadoop), ensure data quality and security, and coach teams to deliver analytics solutions that drive tax practice insights and client outcomes.

Design and build data infrastructure, pipelines, and transformations using Azure Data Factory, Azure Databricks, and Snowflake. Develop data architecture and modeling, maintain data lakes/warehouses, ensure data quality and security, optimize performance, and collaborate with clients to deliver tailored data solutions and governance.

Design and productionize scalable batch and streaming data pipelines and governed data products in Azure Databricks. Build lakehouse architectures, automation, AI-enabled retrieval and semantic workflows, and production data solutions supporting analytics, ML, LLMs, and agents. Establish standards for testing, quality, lineage, observability, security, and cost management while solving complex engineering problems. Partner with data science and ML teams, influence technical direction, mentor engineers, and improve delivery practices.

Designs, develops, and maintains scalable data pipelines, streaming applications, data platforms, and AI-powered solutions. Uses Python, Spark, Scala, Kafka, and cloud technologies to support enterprise healthcare systems. Responsibilities include data modeling, warehousing, database design, performance tuning, workflow automation, vulnerability remediation, platform upgrades, data security, and operational stability. Collaborates with cross-functional teams to translate data requirements into high-performing technical solutions.

Designs, builds, and supports scalable data pipelines, cloud-native processing systems, data models, APIs, and analytics solutions. The role ensures data quality, observability, testing, governance, CI/CD, and production reliability while partnering with engineering, product, analytics, BI, and business stakeholders. Responsibilities include architecture decisions, troubleshooting, documentation, Agile delivery, incident response, and mentoring engineers.

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Leads the design, development, and operation of production-grade data pipelines and governed data products supporting operational resiliency and recovery-risk analytics. Responsibilities include data modeling, ETL, database optimization, data quality and lineage, cloud and event-driven architectures, automation, testing, and CI/CD. Partners with cybersecurity, technology controls, engineers, architects, analysts, and business stakeholders to translate risk and control requirements into scalable, auditable solutions.

Designs and maintains scalable data pipelines, models, integrations, and ETL/ELT processes using cloud data technologies. Analyzes complex data, implements quality controls and automated testing, troubleshoots issues, documents data flows, and improves engineering processes. Collaborates with product owners, business stakeholders, data scientists, engineers, and architects in an agile environment while translating business needs into reliable technical data solutions.

The Data Engineer will design data models, build data warehouses and lakes, automate data pipelines, and enable data accessibility while maintaining security standards in a complex environment.

Design, build, and operate large-scale ETL pipelines, data lakes, and data platforms processing billions of daily events. Own data quality, freshness, reliability, monitoring, and observability for foundational datasets. Develop self-service ETL frameworks and tooling, contribute to architecture and technical strategy, and partner with product, engineering, machine learning, data science, and leadership teams to deliver scalable data solutions.

Designs, develops, deploys, and supports scalable cloud data engineering solutions for HEDIS processing and healthcare quality reporting. Builds ETL pipelines, data integrations, SQL transformations, and AI-enabled workflow automation. Collaborates with stakeholders, auditors, and product teams to ensure data quality, regulatory compliance, and operational reliability. Troubleshoots production issues, supports cloud modernization, applies CI/CD and testing practices, and mentors engineers as a senior individual contributor.

Designs, builds, and maintains secure data pipelines, ETL processes, data warehouses, and quality controls. Collects, validates, cleanses, and analyzes data; develops SQL queries, Tableau dashboards, and AWS-based solutions; investigates data integrity issues; documents standards; and partners with stakeholders to deliver technical solutions using Agile and DevOps practices.

Designs and operates scalable GCP data pipelines, reusable engineering frameworks, feature stores, orchestration workflows, and governed data models supporting AI and machine learning. Builds Python, SQL, and Beam transformation libraries; ensures workload cost, performance, reliability, quality, lineage, and observability; and partners with data scientists migrating models to GCP. The role also documents best practices, provides enablement, and supports production systems during off-hours.