Top Senior Data Engineer Jobs

Design, build, test, and deploy scalable cloud-first data solutions, lakehouse architectures, and real-time streaming pipelines. Lead Agile engineering teams, establish data engineering standards, implement security and governance controls, and optimize systems for quality, performance, and observability. Collaborate with product managers, software engineers, analysts, and data scientists to deliver reusable data products and personalized customer experiences. Apply modern technologies including Python, SQL, Spark, Kafka, Databricks, Snowflake, and interactive AI coding tools.

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.

Designs, builds, and operates scalable cloud data platforms, pipelines, and data products. The role requires architectural leadership across distributed data, lakehouse, streaming, warehousing, relational and NoSQL systems. Responsibilities include selecting platforms such as Snowflake and Databricks, ensuring scalability and resilience, mentoring engineers, influencing technical teams, and delivering transformative data initiatives independently. Candidates need extensive application development, SQL, programming, data pipeline, data modeling, cloud, and distributed data experience.

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 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.

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.

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.

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.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. Lead architectural decisions, data modeling, distributed and streaming workloads, and technology evaluations across platforms such as Snowflake and Databricks. Collaborate with product, engineering, analytics, and data science teams; establish engineering standards; communicate technical outcomes; and mentor engineers while driving large-scale data initiatives independently.

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, and lead scalable cloud data platforms, pipelines, and applications. The role architects data engineering patterns, evaluates platforms such as Snowflake and Databricks, delivers distributed and streaming workloads, and ensures operational resilience. Responsibilities include collaborating with product and engineering teams, influencing technical decisions, mentoring engineers, communicating data concepts, and leading large-scale initiatives independently.

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.

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.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Build and optimize scalable batch and streaming data pipelines for Comcast’s enterprise Data Lake. Develop cloud-native solutions using AWS, Databricks, PySpark, Kafka, Kubernetes, and Airflow/MWAA. Support high-volume distributed data processing, production troubleshooting, data warehousing, automation, observability, and data quality initiatives. Collaborate with product, governance, analytics, and engineering teams, participate in architecture decisions, mentor junior engineers, and improve platform reliability and performance.

Design, develop, and maintain scalable batch and streaming data pipelines using Python, PySpark, SQL, Databricks, Kafka, and Azure Data Factory. Provide production support, incident resolution, vulnerability remediation, platform upgrades, and reliability improvements. Optimize data models and processing workloads, implement automation and AI-enabled solutions, support CI/CD and cloud modernization, and ensure healthcare data security, governance, and privacy compliance.

Develop and maintain master data management and integration capabilities, including data quality rules, governance processes, stewardship workflows, data validation, profiling, modeling, and integration design. Collaborate with enterprise stakeholders, data owners, stewards, and governance groups to define requirements, configure systems, and support strategic MDM initiatives. Use Python, SQL, AWS services, APIs, Dataiku, and ETL/MDM tools to improve data accuracy, consistency, transformation, and retention.

Leads end-to-end data enablement for Supply Chain and Manufacturing domains. Partners with business and technical stakeholders to define use cases, roadmaps, data models, semantic layers, integration requirements, quality controls, and governed data products. Builds and optimizes Databricks lakehouse pipelines using SQL, Python, PySpark, and Delta Lake across complex ERP, manufacturing, procurement, inventory, and finance systems. Establishes standards for lineage, security, documentation, reliability, and production readiness.

Designs and maintains scalable data pipelines, models, integrations, and ETL/ELT solutions using cloud data technologies. Analyzes complex data, implements quality controls and automated testing, troubleshoots data issues, documents data flows, and supports governance. Collaborates with product owners, stakeholders, architects, data scientists, and engineers in an agile environment while applying AI-assisted development responsibly.

Designs, builds, and operates batch and real-time connected-vehicle data pipelines and products. Develops streaming systems, APIs, cloud-native workloads, observability, and secure data capabilities using Azure, Flink, Spark, Java, Python, and SQL. Applies machine learning and generative AI, including retrieval-augmented generation and agentic workflows, to data engineering and operational problems. Participates in architecture, testing, incident response, on-call support, mentoring, and cross-functional technical leadership.