Top Senior Data Engineer Jobs

Design, build, modernize, and maintain operational and analytical data capabilities for Wealthscape Reporting, Analytics and Insights. Perform solution design, data analysis, ETL development, and production rollouts using Snowflake, AWS, cloud databases, and CI/CD pipelines. Collaborate across teams in a fast-paced financial services environment.

The Data Engineer will develop data-driven decision analysis products, provide analytic consulting, support stakeholders with tools, and optimize defense systems for national security.

The Data Integration Lead designs and manages data ingestion pipelines, ensuring data integrity and security while supporting ETL processes in classified environments.

The Principal Data Engineer will lead development projects, design scalable data pipelines, ensure data quality, and mentor a team of engineers while collaborating with stakeholders.

Develop and implement large-scale Big Data solutions using Java, Hadoop, NoSQL, and MPP technologies. Design and optimize distributed, data-intensive systems, participate in architecture discussions, collaborate with senior architects and offshore teams, and deliver high-quality projects. Engage with clients, conduct technical sessions, create documentation and case studies, and share knowledge across the organization.

Lead Data Engineer responsible for building, optimizing, and operating secure, scalable data pipelines and platforms. Develop and run batch and streaming pipelines, workflow orchestration (Airflow), SQL-based transformations, and production-grade Python/PySpark code. Collaborate with analysts and data scientists, own system reliability and security, mentor junior engineers, and apply enterprise-authorized AI tools with strong validation and data sensitivity practices.

Design, build, and maintain scalable data lakes, warehouses, and production ETL/ELT pipelines. Automate data pipelines, ensure data quality via testing, collaborate with architects, data scientists, and DevOps, integrate data solutions into client environments, and implement data recovery and security measures.

Build and maintain production-grade data processing systems and foundational infrastructure for enterprise AI. Collaborate with researchers and engineers, deliver end-to-end implementations, transform unstructured data into performant datasets, and use distributed processing frameworks and modern analytics tooling to inform products and strategy.

Lead data engineering efforts, manage data governance, ensure compliance, and oversee data platform economics while designing scalable systems.

Design, build, and maintain scalable data pipelines and models for Finance and operations; collaborate with analysts and data scientists to provision trusted analytics; ensure governance, performance, testing, and incident root-cause fixes; recommend technologies and mentor team members to adopt cloud-first, best-practice data architectures.

Lead implementation of ETL patterns and reliable data infrastructure, partner with business and engineering teams to prioritize and deliver integrations, APIs, and reusable components, and support operational data applications in an Agile environment.

Design and build a modern enterprise data platform supporting analytics, reporting, machine learning, and near-real-time processing. Responsibilities include architecting warehouse or lakehouse solutions, modernizing legacy ETL, developing CDC and incremental pipelines, creating semantic layers, implementing data quality and observability frameworks, and establishing governance and retention strategies. The role also provides technical leadership and partners with Engineering, Product, Analytics, and Data Science teams.

Leads the design, implementation, and optimization of cloud-native data infrastructure, platforms, storage solutions, and complex data pipelines. Establishes engineering standards for testing, monitoring, releases, and reliability; troubleshoots cross-system failures and leads incident reviews. Partners with infrastructure, laboratory IT, data science, bioinformatics, and scientific teams to deliver secure, scalable solutions. Mentors engineers, guides technical initiatives, and uses generative AI to accelerate development and data transformation.

Design, build, and operate scalable data platforms and pipelines; ingest, clean, query, and store disparate DoW/IC data; select and optimize SQL/NoSQL backends; lead cross-functional teams in agile environments; maintain data management systems for mission-driven analytics.

The Data & AI Engineer III will lead cloud data engineering solutions, develop data infrastructure, establish architectures, mentor team members, and secure data around cloud systems.

Design and implement data cleaning, preprocessing, integration, and quality checks. Develop dashboards and complex visualizations (Tableau/Power BI). Apply AI/ML techniques for unstructured data analysis and provide data engineering support for visualization and analytics.

Build and operate Sinclair’s enterprise Snowflake data platform, developing production pipelines, integrations, ELT models, data quality controls, observability, governance, and performance optimizations. The role requires hands-on Snowflake, Openflow, SQL, and Python expertise, production troubleshooting, enterprise integrations, deployment practices, and collaboration across technical and business teams. This is an on-site position in Hunt Valley, Maryland.

Design, build, and operate scalable batch, streaming, and CDC data pipelines across Snowflake and AWS. Lead ETL/ELT architecture, orchestration, data modeling, quality monitoring, observability, incident resolution, and performance optimization. Develop production solutions in SQL and Python, establish engineering standards for testing and CI/CD, and partner with Analytics Engineering, Data Science, Product, and Engineering teams to deliver reliable data products.

Design, build, and operate large-scale data platforms and production ETL/ELT pipelines. Partner with data science, analytics, and business teams; manage cross-functional delivery, QA, observability, data quality, experimentation infrastructure, and statistical model validation. Deploy containerized workloads through Docker, Kubernetes, CI/CD, and Helm. Own data operations, monitoring, incident response, SLAs, runbooks, and pipeline reliability while delivering accurate, scalable, and cost-efficient data assets.

Build scalable data solutions and reconciliations to automate Compliance & Finance regulatory processes. Partner with stakeholders to translate requirements into designs, lead technical development, review code, and evolve standards for data pipelines and reporting.

Designs and operates data-driven solutions supporting intelligence requirements. The role develops data architectures, integration models, automation, algorithms, and software tools; collaborates with GEOINT analysts; manages data governance and metadata; interrogates databases with SQL; and applies data science libraries to large-scale intelligence analysis. It requires substantial intelligence and GEOINT experience, advanced technical expertise, strong communication skills, U.S. citizenship, and an active TS/SCI clearance.

Designs, builds, and operates scalable batch and real-time data infrastructure processing trillions of data points. Responsibilities include platform architecture, infrastructure as code, self-healing systems, observability, developer tooling, pipeline optimization, security controls, database tuning, and distributed-system troubleshooting. The role collaborates with engineering and product teams, improves reliability and performance, and enables safe access for data engineers, software engineers, and AI agents.

Design, build, and maintain scalable data pipelines and platforms to ingest, merge, and prepare structured and unstructured data for analytics and AI. Lead multidisciplinary teams, perform data exploration and statistical analysis, and support use of LLMs/GenAI to enable mission-driven solutions across fraud detection, healthcare, and intelligence domains.

Build and operate large-scale data ingestion pipelines powering research and trading platforms. Own pipeline quality, reliability, performance, and data health; collaborate with traders, quantitative researchers, and data owners; optimize compute, storage, and SLAs; migrate pipelines to modern platforms; and implement tools to detect and resolve source-data quality issues.

Lead design, build, and support of scalable data pipelines and integrations using Microsoft Fabric, ADF, PySpark, SQL, ADLS Gen2, and Databricks Unity Catalog. Drive Azure-to-Fabric migration, stabilize pipelines, tune performance, manage data governance, troubleshoot issues, document designs, mentor team members, and collaborate with stakeholders in Life Sciences regulatory environments during US Eastern hours.