Top Senior 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.

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.

Design, build, and lead scalable cloud data pipelines, platforms, and end-to-end data solutions. The role involves distributed data engineering, data modeling, relational and NoSQL databases, real-time processing, warehousing, architecture decisions, and evaluating platforms such as Snowflake and Databricks. Responsibilities also include mentoring engineers, enforcing engineering standards, collaborating across Agile teams, and delivering transformative data initiatives independently.

Designs, builds, and supports scalable data warehouse solutions, including pipelines, transformations, data models, governance practices, metadata, and documentation. Develops source-to-target mappings, DDL documentation, and SCD models; troubleshoots data issues and performs root cause analysis. Collaborates with engineering, quality assurance, reporting, and business teams on data migrations, modernization, releases, and code reviews. Leads data requirements analysis and data solution development across multiple platforms.

Designs, builds, and maintains data warehouse solutions, pipelines, models, transformations, and data services across multiple platforms. Leads data requirements analysis, governance, metadata management, source-to-target mapping, migration, modernization, troubleshooting, and root-cause analysis. Collaborates with engineering, business, quality, and reporting teams to deliver scalable, accurate, secure, and maintainable data solutions supporting asset management operations.

Leads the design, development, and maintenance of scalable data warehouse solutions, including pipelines, data models, transformations, mappings, and documentation. Supports data governance, metadata management, migration, modernization, troubleshooting, and root cause analysis. Collaborates with engineering, quality, business analysis, and reporting teams to deliver reliable data solutions across multiple platforms and applications.

Design and develop data pipelines, integrations, transformations, and infrastructure across AWS, Snowflake, Airflow, and MySQL. Troubleshoot warehouse issues, validate data quality, improve pipeline reliability, and translate business needs into data engineering solutions. Interpret technical findings for client recommendations, manage workstreams, review junior team members’ work, and provide technical guidance across managed services engagements.

Provides data architecture leadership for Payments Operations, defining data models, metadata, authoritative sources, quality rules, and data flows. Designs and implements analytics and data delivery capabilities, profiles and transforms data using Python, PySpark, and SQL, and evaluates data fitness for business processes. Partners with technology, product, analytics, and data teams to develop solutions, apply governance standards, and research emerging technologies.

Designs and maintains reusable data ingestion and transformation components, automated quality and governance checks, reliable pipelines, monitoring, deployment automation, cloud integrations, containers, and infrastructure as code. Partners with data consumers on usable models and datasets, documents standards and runbooks, supports Agile improvement, and responds to critical IT incidents. Requires 5+ years of data engineering experience, orchestration, distributed processing, storage, scripting, automation, and version control expertise.

Designs and implements reusable data ingestion and transformation components, automated quality and governance checks, reliable pipelines, monitoring, alerting, and observability practices. Supports CI/CD, cloud integrations, container orchestration, infrastructure as code, deployment automation, data modeling, documentation, and Agile platform operations. The role also contributes to incident response and critical escalations.

Leads development and operation of secure, scalable data platforms, pipelines, and data products. Designs AWS-based data engineering solutions using Python, Spark, Databricks, and related services; develops semantic layers with Iceberg and APIs; and implements CI/CD with Terraform. Responsibilities include code development and review, troubleshooting, access-control evaluation, resiliency, automation, and enterprise AI-assisted workflows. Collaborates within agile teams to support large-scale analytical and operational data use cases.

Designs and leads scalable Azure data platforms, ETL/ELT pipelines, Spark workflows, AI integrations, and cloud applications. Responsibilities include data modeling, advanced SQL optimization, reliability improvements, API integration, AI-powered automation, architectural standards, stakeholder analysis, mentoring, and evaluating emerging technologies. The role uses Azure Databricks, Snowflake, Spark, Python, and modern software engineering practices to support analytics, machine learning, business intelligence, and healthcare data solutions.

Designs and manages enterprise finance data architectures, pipelines, transformations, semantic layers, retrieval indexes, and reusable data products. Partners with AI engineers on RAG, embeddings, vector databases, and data refresh processes. Evaluates data quality, lineage, security, privacy, and governance while developing synthetic or de-identified datasets. Implements monitoring, validation, exception handling, and experimentation datasets, and helps guide emerging data and AI architecture decisions.

Designs, builds, and operates scalable cloud data platforms, pipelines, and applications. Leads architecture decisions, data modeling, distributed and streaming workloads, and data engineering initiatives. Collaborates with product managers, engineers, analysts, and data scientists; evaluates platforms such as Snowflake and Databricks; establishes reusable engineering patterns; communicates technical outcomes; and mentors other engineers.

Develop scalable data pipelines, analytics solutions, and full-stack applications using Java or Python, AWS, Databricks, and Spark. Analyze large datasets, design cloud-native architectures, collaborate cross-functionally, and explore Agentic AI and AI-assisted development tools.

Design, build, and maintain large-scale ETL pipelines and data warehouses (Redshift) with strong data modeling, governance, and quality controls. Optimize systems for performance and reliability, integrate and deploy AI/ML models, troubleshoot and support Redshift, collaborate with data scientists and stakeholders, and produce technical documentation and knowledge-base articles.

Build and architect scalable cloud data platforms, pipelines, and applications using technologies such as Python, Spark, Databricks, and Snowflake. Lead large-scale data initiatives, make architectural decisions, establish engineering patterns, and ensure platform resilience and operational efficiency. Collaborate with product, engineering, analytics, and data science teams while mentoring engineers and communicating technical concepts to stakeholders. Evaluate data platform choices and deliver solutions supporting machine learning, distributed systems, and evolving enterprise data needs.

Lead large-scale data engineering initiatives, designing scalable cloud data platforms, pipelines, and architectures. Use Python, Spark, SQL, Databricks, Snowflake, AWS, relational and NoSQL databases to deliver resilient data solutions. Mentor engineers, analysts, and data scientists; establish engineering standards; evaluate platform choices; and collaborate with product and software teams to support enterprise data transformation and AI capabilities.

Leads the design, development, implementation, and support of scalable data solutions across multiple applications and platforms. Responsibilities include data requirements analysis, ETL/ELT development, data modeling, migration and transformation, workflow orchestration, performance optimization, data quality, pipeline monitoring, and production support. The role also provides technical leadership through coding standards, code reviews, mentorship, and engineering best practices.

Build reliable ETL infrastructure, scalable data pipelines, and trusted datasets serving product and business teams. Partner with data scientists and stakeholders to model data, monitor pipelines, define delivery SLAs, automate workflows, and enable self-service analytics and AI-assisted data exploration. The role requires strong SQL, cloud data-platform expertise, object-oriented programming, data modeling, and experience delivering production-scale data solutions.

Designs, builds, and operates scalable, resilient cloud data platforms and pipelines. Leads large-scale data initiatives, makes architectural decisions, evaluates technologies such as Snowflake and Databricks, and develops solutions using distributed data systems, relational and NoSQL databases, and cloud platforms. Collaborates with product and engineering teams, establishes reusable design patterns, communicates technical concepts, and mentors data engineers, analysts, and scientists.

Designs and scales enterprise data platforms, pipelines, and integration frameworks for structured and unstructured data. Builds Snowflake and cloud-based AWS/GCP solutions, leads legacy-platform modernization and ETL/ELT implementation, and develops AI-ready datasets and data products. Establishes data governance, security, monitoring, and CI/CD practices for reliable, compliant platforms supporting analytics, machine learning, generative AI, and automation.

Designs reusable data ingestion and transformation components, automates data quality, governance, lineage, and deployment checks, and improves pipeline reliability through monitoring and observability. The role supports cloud integrations, container orchestration, infrastructure as code, documentation, Agile planning, and incident response while partnering with data consumers to deliver usable datasets. Requires 5+ years of data engineering experience, distributed processing and storage expertise, scripting, automation, and version control skills.

Designs reusable data ingestion and transformation components, automates data quality, governance, testing, lineage, and deployment workflows, and improves pipeline observability and reliability. The role supports monitoring, incident response, cloud and container integrations, infrastructure as code, documentation, reusable datasets, and Agile platform improvements. Candidates need five or more years of data engineering experience plus expertise in orchestration, distributed processing, storage, scripting, automation, and version control.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.