Top Data & Analytics Jobs

The Principal Data Engineer will lead data architecture initiatives, design scalable data systems, and oversee data standards across teams while ensuring high-quality data infrastructure and guiding engineering teams.

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.

As a Business Intelligence/Data Engineer, you will analyze data structures, implement data warehousing solutions, and design data visualizations while working with AI/ML services in cloud environments.

Design, build, and maintain an internal data platform and pipelines. Ensure secure, governed, resilient data access for engineering and BI teams. Partner with architects, implement automations, support platform consumption, mentor users, and drive continuous improvement and compliance.

The Data Engineer will design and implement scalable data platforms, build data pipelines, collaborate with teams to ensure data quality, and streamline processes using AWS services and big data technologies.

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.

Build and maintain marketing data pipelines, replace spreadsheet workflows with structured databases, automate data cleaning/deduplication and syncing, develop AI-driven reports and dashboards, and collaborate with marketing, AdOps, and engineering to ensure reliable, unified, AI-ready marketing data.

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.

Support and maintain cloud-based data pipelines and warehouses (Redshift/AWS). Develop ETL, data ingestion, and validation processes, collaborate with data scientists to deploy models, ensure performance and reliability, automate ingest workflows, and follow agile and CI practices while documenting evolving requirements for a DHS federal customer.

The Data Engineer designs and maintains scalable data management systems, implements data governance, and builds ETL workflows to support analytics and reporting needs.

Design, build, and maintain scalable Databricks-based data pipelines (PySpark, SQL) to process shipment, sell-out, and market share data. Collaborate with architects and product teams, reduce technical debt, provide L3 operational support, and deliver solutions within an Agile/Scrum environment.

Design, harden, and scale Snowflake and AWS data platforms, build dimensional data models and resilient Airflow pipelines, optimize complex SQL, develop dbt semantic layers, deploy ML models in AWS, integrate AI/LLMs into workflows, and build data APIs (FastAPI/Flask) to enable enterprise analytics and operational ROI.

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.

Lead and mentor engineering teams to design, build, test, and support cloud-based data solutions. Work with big data, streaming, and data warehousing technologies, collaborate with product managers, and ensure performant, well-tested code and architectures.

Lead and mentor a team of data engineers to design, develop, test, implement, and support cloud-based data platforms and full-stack solutions. Build distributed, real-time, and batch data systems using Java/Scala/Python, big data tools (Spark, Hadoop, Kafka), NoSQL and RDBMS, and data warehouses (Redshift, Snowflake). Collaborate with Agile teams, perform unit tests and code reviews, and drive technology experimentation and transformation.

Design, develop, and maintain Customer Data Platform solutions and data pipelines to create unified customer profiles, manage identity resolution, enable audience segmentation and activation, ensure data quality and governance, and support integrations with marketing, CRM, and cloud data systems.

Build and lead NVIDIA’s enterprise data governance platform, covering sensitive-information detection, governed access, export-control enforcement, audit logging, data connectors, and AI knowledge readiness. Design scalable backend, distributed, and data-processing systems; integrate enterprise content sources; evaluate ML classification models; enforce access-control fidelity; and ensure ingestion quality. Provide technical leadership through architecture decisions, code and design reviews, mentorship, and cross-functional alignment with Security, Legal, Finance, and platform teams.

Design, build, and operate scalable batch and real-time data pipelines on Databricks/Azure, implement medallion architecture with Delta Lake, ensure data quality and governance via Unity Catalog, support CI/CD and production operations, and collaborate with stakeholders to deliver analytics-ready datasets for BI and ML use cases.

Works on large-scale data and analytics projects by acquiring, integrating, analyzing, and interpreting complex datasets. Identifies trends, applies statistical techniques and predictive modeling, develops actionable recommendations, presents findings to stakeholders, monitors outcomes, and ensures data practices comply with company standards.

Design, implement, and maintain Azure-based data pipelines and analytics-ready datasets. Develop transformations, optimize SQL, contribute code across the platform, support production workloads, apply engineering patterns, work with stakeholders, and support CI/CD, testing, and monitoring for analytics and AI/ML use cases.

Designs and builds modern data applications, pipelines, transformations, enrichment processes, provisioning layers, and interfaces supporting clinical and operational healthcare processes. Analyzes clinical data to identify trends, collaborates with Product, Platform, and Architecture teams, mentors junior data engineers, and evaluates emerging technologies. The role emphasizes cloud computing, big data, data science, modern development frameworks, source control, documentation, and repeatable engineering practices.

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.