Top Senior Data Engineer Jobs

Design, develop, and maintain data pipelines and software solutions to process and combine diverse data sources. Perform statistical analysis (regression, anomaly detection, clustering), automate and optimize data products, communicate findings to technical and non-technical stakeholders, elicit system requirements, validate performance, and support time-sensitive intelligence operations.

Lead the architecture and development of Vercel’s Finance Data Platform supporting revenue recognition, consumption-based billing, forecasting, reporting, and compliance. Build reliable batch and real-time pipelines using Kafka, ClickHouse, Tinybird, and Snowflake; establish governance, lineage, auditability, monitoring, and fault tolerance; translate Finance and Accounting requirements into scalable systems; guide architecture and roadmap decisions; collaborate with Security, Legal, Compliance, Audit, FP&A, and RevOps; and mentor data engineers.

Lead the architecture, development, and optimization of Snowflake and enterprise data platforms. Design scalable ETL/ELT pipelines, dimensional data models, and medallion architectures while establishing engineering standards for testing, deployment, monitoring, security, and data quality. Partner with stakeholders on technical roadmaps, mentor engineers and analysts, conduct design and code reviews, modernize legacy pipelines, and deliver reliable, reusable data solutions.

Own and evolve the enterprise data platform by architecting scalable data models, integrations, warehouses, and pipelines. Build and maintain solutions using Snowflake, dbt, Fivetran, Airflow, AWS, and Power BI. Integrate diverse data sources, implement monitoring and data quality frameworks, troubleshoot production issues, and deliver curated datasets for analytics and AI use cases. Provide technical leadership, establish engineering standards, and collaborate with business, governance, QA, and analytics teams.

Lead data engineering activities by designing, building, deploying, and maintaining scalable data platforms and pipelines. Mentor multidisciplinary teams, oversee analytical data examination, guide enterprise software development, establish execution priorities, and support complex distributed applications in agile environments. The role requires Java, Spring, Linux, containers, CI/CD, and a TS/SCI clearance with polygraph.

Designs, builds, and maintains data pipelines, ETL processes, integrations, data models, schemas, and warehouse systems. The role optimizes data infrastructure for performance and scalability, implements governance, security, validation, and cleansing practices, and provides technical leadership to data engineering teams. It collaborates with clients, stakeholders, data scientists, analysts, and cross-functional teams to deliver reliable data solutions and evaluate emerging technologies.

Design, build, and iterate data engineering pipelines, dashboards, and reports to support analytics and data science. Partner with stakeholders to translate requirements into engineering solutions, promote self‑service analytics, define best practices, and support implementation using Databricks, Snowflake, and Azure components.

Own and build the company’s data warehouse architecture, curated and semantic layers, data models, quality standards, and migration automation. Develop Redshift-based pipelines using Python, PostgreSQL, Fivetran, dbt, AWS, Airflow, and Terraform. Establish metrics governance, observability, infrastructure-as-code, AI-enabled enrichment, anomaly detection, metadata generation, and structured clinical-record ingestion. Partner with engineering, product, and commercial teams to define metrics and reliable platform change contracts while mentoring data engineers.

Develop and optimize Oracle Fusion Data Intelligence analytics, reporting, semantic models, data integrations, and augmentation solutions. The role supports Oracle ERP, HCM, and SCM data, including OTBI, BI Publisher, ODI, ETL, SQL, and PL/SQL development. Responsibilities include requirements gathering, data cleansing and validation, cross-functional collaboration, and contributing to an ERP analytics roadmap. This is an onsite role in Des Moines, Las Vegas, or Portland, with an initial 3–4 month W2 engagement.

Designs and optimizes scalable data pipelines, Snowflake data warehouse solutions, and dbt transformation frameworks. Develops dimensional and enterprise data models, ETL/ELT processes, quality tests, documentation, and governance practices. Tunes Snowflake performance and costs, supports migrations and integrations, and collaborates with stakeholders, analysts, architects, and engineers to deliver analytics and reporting solutions.

Lead enterprise data platform architecture across ingestion, storage, processing, governance, security, and consumption. Establish standards for data modeling, lakehouse and streaming patterns, pipelines, lineage, cataloging, disaster recovery, and cost optimization. Partner with engineering, ML, BI, product, and business teams; lead architecture reviews, major platform initiatives, and mentoring while producing architecture artifacts and guiding scalable, reliable data infrastructure.

Design and build PerfectServe’s unified cloud data platform, including ingestion pipelines, integrations, data models, metrics, and consumer-facing data layers. Establish architecture, engineering, quality, and testing standards; enable safe extraction from production systems; partner with data users; and mentor data engineers. The role requires substantial cloud data platform experience, deep SQL, warehouse expertise, ELT, change-data-capture, AWS, modeling, orchestration, and data quality skills.

Owns enterprise data architecture and scalable Databricks pipelines using medallion architecture and lakehouse formats. Designs dimensional models, star schemas, data marts, and the semantic layer; governs data quality and reliability; optimizes infrastructure and cloud costs; prioritizes deployments; collaborates with stakeholders; and mentors a junior data engineer.

Build and operate production data pipelines that ingest, transform, validate, and deliver messy clinical data for machine learning, analytics, and clinician-facing APIs. Own data contracts, monitoring, backfills, failure recovery, and troubleshooting across systems. Collaborate with integration, ML, and product engineering teams to ensure reliable data delivery across hospital deployments.

Designs, develops, and maintains large-scale data processing systems, including ETL processes, data models, scalable pipelines, databases, data warehouses, and cloud-based big data solutions. Uses Snowflake, Fivetran, dbt, and Airflow while applying DevOps and security practices. Optimizes distributed processing and storage systems, collaborates with data scientists and analysts, and mentors junior engineers.

Develops and supports Oracle database solutions, including complex SQL and PL/SQL objects, performance tuning, security, data governance, quality management, and migration initiatives. Performs ETL/ELT, data validation, reconciliation, conversion, and post-migration verification while maintaining metadata, lineage, documentation, and data dictionaries. Collaborates with data owners, developers, DBAs, business users, and project managers. Requires five or more years of Oracle development experience and an active TS/SCI clearance with polygraph.

Designs, builds, operates, and improves large-scale data pipelines and foundational data products supporting healthcare analytics, customer applications, and AI/ML use cases. Responsibilities include transforming healthcare datasets, improving reliability and performance, implementing data quality and observability, debugging production workflows, contributing to architecture and CI/CD, and collaborating across engineering and product teams. The role also involves mentoring, technical design reviews, and production support.

Leads the architecture, development, governance, and operational excellence of AssetMark’s Azure and Snowflake data platform. Designs scalable ETL/ELT pipelines, optimizes performance and costs, establishes DataOps, observability, lineage, security, and SLAs, and mentors engineers. Partners with data science and product teams to enable machine learning, MLOps, feature engineering, and generative AI capabilities. Provides hands-on coding in Python and SQL while guiding technical strategy, standards, code reviews, CI/CD, and platform innovation.

Leads research, design, development, testing, deployment, and sustainment of advanced data engineering solutions using GenAI. Designs data pipelines, structures, databases, and architectures; translates business requirements into technical tasks; leads code reviews and project planning; collaborates across teams; and performs hands-on development using SQL, Python, cloud, containerization, orchestration, and source-control technologies.

Build and operate Grassroots Carbon’s unified data platform, including AWS-based storage, production ingestion pipelines, scheduled datasets, integrations, model deployment, monitoring, security, and access controls. Migrate and reconcile historical PastureMap data while establishing lineage, shared identifiers, validation, and reproducible workflows. Partner with science, product, backend engineering, and operations teams to productionize models and deliver reliable data services supporting soil carbon modeling, grazing management, and rancher-facing products.

Drive architecture and standards for scalable data infrastructure, pipelines, and data management. Build cross-functional data models and reliable batch and streaming workflows, including ingestion of financial and transactional data. Use SQL, Python, cloud data platforms, orchestration tools, and transformation frameworks to ensure data quality, monitoring, compliance, and actionable insights. Lead initiatives, collaborate with stakeholders, and mentor engineers.

Build and operate scalable data infrastructure, including fault-tolerant pipelines, data discovery services, and data lineage systems. Develop backend services that process petabytes of data for machine learning and autonomy teams. Lead system design, code reviews, and technical contributions while using AWS cloud ETL technologies and distributed systems. Collaborate with stakeholders to improve platform adoption and address data customer needs.

Build and own cloud-based data pipelines and governed data products supporting Employee Benefits Sales and Underwriting. Design ELT solutions, integrate complex data, improve data quality, and implement CI/CD and enterprise data engineering practices. Architect hybrid cloud solutions using AWS, Hadoop, Spark, Kafka, Snowflake, and related technologies. Influence architecture and tooling decisions, document technical guidance, mentor teams, evaluate emerging technologies, and contribute to strategic roadmaps.

Leads data engineering, advanced analytics, and AI initiatives supporting engineering productivity and customer value. Translates engineering, manufacturing, and customer requirements into data tools, analytic workflows, visualizations, APIs, and scalable AI solutions. Oversees data monitoring, predictive and prescriptive analytics, agentic workflows, stakeholder engagement, agile delivery, and cross-functional projects while leading a small team and promoting documentation and knowledge sharing.

Lead hands-on development and operation of production data pipelines and platforms using PySpark, Python, SQL, and AWS services. Translate requirements into technical designs, data models, SLAs, and reliable pipeline architectures. Establish engineering, data quality, observability, documentation, and coding standards; conduct code reviews; mentor engineers; manage Agile delivery and releases; and use GenAI tools to improve productivity and solution quality.