Top Data & Analytics Jobs

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.

Build and optimize scalable Databricks data pipelines across a medallion architecture. Develop data models, implement data quality checks and validation, integrate Azure services, and ensure data accuracy, governance, lineage, security, and performance. The role requires designing config-driven pipelines supporting CDC and event-based processing, maintaining CI/CD deployments, and working with structured and unstructured data across SQL and NoSQL systems.

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 maintains scalable data pipelines, ETL/ELT processes, metadata repositories, catalogs, and governance solutions. Integrates structured and unstructured data across cloud platforms, develops data warehouses, lakes, and lakehouses, and implements lineage, quality monitoring, automation, and compliance controls. Collaborates with architects, analysts, data scientists, governance teams, and business stakeholders to support analytics, AI/ML, business intelligence, data discovery, and self-service reporting.

Own product telemetry, maintain dbt models in Snowflake, support self-service reporting and NLP queries, drive product insights from user and retention data, and monitor Airbyte syncs to ensure data quality.

Sets technical direction and architectural coherence across Netflix’s Ads Data Engineering organization. The role guides cross-domain data system decisions, partners with engineering and business leaders, mentors data engineers, shapes platform-wide initiatives, and advances AI-native data engineering. Responsibilities span modern data foundations, ad-tech data flows, privacy and governance, platform migrations, scalable data products, and collaboration with Ads Platform Engineering, Data Science, and Product teams.

Leads architecture, optimization, scaling, and ownership of Zoox’s enterprise and AI data platform. Builds self-healing pipelines, governance frameworks, data quality and observability systems, secure access controls, lifecycle policies, and automated CI/CD deployment infrastructure. Optimizes Databricks and AWS EMR performance and costs while enabling low-latency, AI-ready data foundations. The role also drives enterprise security, compliance, schema evolution, and developer velocity, with opportunities to apply GenAI, Terraform, and EKS.

Design and implement a financial knowledge graph architecture, build graph ingestion and transformation pipelines, apply graph algorithms and analytics, define best practices, drive adoption across teams, and mentor/hire engineers to scale the platform while collaborating with investment and compliance stakeholders.

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.