Top Data & Analytics Jobs

Build and maintain recruiting data pipelines, datasets, integrations, dashboards, automations, and data quality controls. Partner with recruiting, data, security, privacy, engineering, and business teams to improve operational reporting and workflows. Responsibilities include integrating ATS and HR systems, monitoring pipeline stability, supporting writeback processes, designing secure access solutions, documenting sensitive workflows, and adapting data products as recruiting processes evolve.

Lead data engineering efforts supporting secure, scalable data collection, storage, access, and analytics platforms. Responsibilities include building data models, maintaining critical pipelines and architectures, implementing database backup, recovery, and archiving strategies, and reviewing data security and access controls. The role also applies and validates enterprise-authorized AI capabilities in data engineering workflows, requiring expertise across the data lifecycle, relational and NoSQL databases, and advanced statistical, geometric, and linear algebra algorithms.

Own the architecture, development, and operations of Cargill’s enterprise data platform. Build and optimize Azure and on-premises ETL/ELT pipelines, manage databases and migrations, implement CI/CD, improve performance and reliability, and create embedded Superset dashboards. Monitor platform health, data quality, security, and costs while integrating emerging AI capabilities. Mentor data professionals and make architectural decisions across global teams.

Designs, develops, and maintains scalable data pipelines, transformations, datasets, and curated data products for credit risk, reporting, analytics, decisioning, and machine learning. The role uses SQL, Python, Databricks, and Spark; implements data quality controls; supports production monitoring and troubleshooting; collaborates with analytics, data science, product, and engineering teams; and contributes to cloud migration and data platform modernization.

Build and architect scalable cloud data platforms, pipelines, and applications using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Collaborate with engineering, product, analytics, and data science teams; implement secure and resilient data solutions; enforce reusable engineering patterns; communicate technical outcomes; mentor colleagues; and support distributed, real-time, and cloud-based workloads.

Design, build, and operate scalable cloud data pipelines, platforms, and applications using distributed data technologies. Lead transformative data initiatives, make architectural decisions, evaluate platforms such as Snowflake and Databricks, and ensure reliability and maintainability. Collaborate with product managers, engineers, analysts, and data scientists while mentoring peers and communicating technical outcomes to stakeholders. Apply data modeling, relational and NoSQL databases, machine learning-enabled solutions, and modern cloud architectures to support risk management products.

Process and validate aircraft monitoring data, decode avionics bus data, troubleshoot C-17 systems, perform root cause analysis, develop data-mining algorithms, and provide technical recommendations to customers. The role supports program reviews, integrated product teams, design reviews, and aircraft health management for the C-17 fleet.

Designs, builds, and maintains scalable data infrastructure, streaming and batch pipelines, data systems, data models, and cloud-based data products. Develops automated deployment pipelines, evaluates architectures, supports data governance, and collaborates with analytics teams and stakeholders. The role requires advanced programming and SQL expertise, modern data architecture experience, and proficiency with cloud platforms, Spark, Kafka, orchestration tools, and data modeling frameworks.

Designs and governs scalable data and AI platforms, including batch and streaming pipelines, automation, infrastructure as code, security, observability, and cloud services. Builds reusable developer tooling and golden paths, improves reliability through monitoring and SLOs, and guides architecture decisions across complex programs. The role also coaches data teams, documents standards, and supports critical incidents.

Designs scalable data ingestion, transformation, storage, and delivery platforms. Builds automation for data quality, lineage, governance, CI/CD, security, observability, and infrastructure deployment. Develops internal tooling and reusable patterns, integrates cloud and container services, maintains infrastructure as code, and improves platform reliability. Provides technical leadership, reviews architectures, documents standards, coaches data teams, and supports critical incidents.

Designs, builds, and operates a scalable data platform supporting entity resolution, knowledge graphs, real-time risk assessment, APIs, and batch or streaming pipelines. Owns data reliability, performance, observability, governance, and platform SLAs. Collaborates with data science, analytics, product, and engineering teams while implementing cloud-native infrastructure, automation, CI/CD, and self-service data capabilities.

Administer, architect, secure, and optimize PostgreSQL, MySQL, and Snowflake databases in Azure. Responsibilities include performance tuning, automation, access control, disaster recovery, documentation, and database solution design for healthcare applications. The role collaborates across Scrum teams, presents technical solutions, evaluates emerging technologies, uses approved AI tools, resolves complex database issues, and mentors junior engineers.

Build and lead large-scale cloud data engineering initiatives, including cross-cloud migrations, lakehouse architecture, scalable data pipelines, data modeling, and real-time data solutions. Use Python, Spark, Databricks, Snowflake, SQL, and relational and NoSQL databases. Independently make architectural decisions, evaluate platform choices, mentor engineers, collaborate with product and software teams, and deliver reliable data platforms powering personalized travel experiences.

Build and architect scalable cloud data platforms, pipelines, and applications using Python, Spark, Databricks, SQL, and relational and NoSQL databases. Lead multi-cloud data migration into a centralized data lake, enforce engineering standards, implement security controls, support real-time data workloads, and collaborate with product, engineering, analytics, and data science teams. The role also includes mentoring, stakeholder communication, and delivering data solutions that support personalized travel products.

As a Quantitative Developer, you'll innovate and enhance quantitative methods, develop custom tools, maintain data pipelines, and collaborate with researchers for advancing investment strategies.

Develop and maintain secure, scalable data pipelines, architectures, models, and analytics solutions. Review enterprise data controls, customize tools and products, validate AI-assisted engineering outputs, and ensure SDLC quality, auditability, resiliency, and security. Use advanced SQL, NoSQL databases, Python, and statistical analysis to support business needs and collaborate with Product Owners. AWS and modern front-end experience are preferred.

Leads the design, development, and operation of scalable enterprise data products using Databricks and modern data engineering practices. Provides hands-on technical leadership across architecture, implementation, testing, troubleshooting, governance, observability, and production readiness. Partners with technical and business stakeholders, establishes engineering standards, promotes responsible AI-assisted development, mentors engineers, and supports analytical, operational, reporting, and AI-driven use cases.

Leads the architecture, administration, engineering, and strategic evolution of enterprise Hadoop-based data platforms. Owns cluster lifecycle, scalability, reliability, security, disaster recovery, monitoring, optimization, upgrades, and operational standards. Troubleshoots distributed systems, supports data ingestion and governance, guides hybrid cloud strategies, documents platform operations, and mentors engineering teams. The role also develops data views, exports, and automated processing while collaborating with data engineering, analytics, security, and infrastructure teams.

Leads end-to-end clinical data exchange implementations with healthcare providers, vendors, laboratories, and HIEs. Responsibilities include requirements gathering, data integration development, testing, deployment, production support, compliance with healthcare data standards, dashboard development, operational metrics, automation, incident management, and mentoring. The role requires coordinating multiple concurrent technical implementations and translating business needs into scalable clinical data solutions.

Design and implement data ingestion patterns, scalable data-sharing solutions, and automated Azure data pipelines for migrating a legacy warehouse to a Databricks lakehouse. Use Azure Data Factory, dbt, Medallion Architecture, Java, Python, and AI-assisted development tools while collaborating across teams to ensure secure, scalable, high-performing solutions.

Lead the architecture and standards for Adyen’s data foundations. Design shared data models, semantic layers, data contracts, lineage, and data products; establish metric definitions and quality standards across domains. Build prototypes, solve complex data-stack challenges, influence teams through technical leadership, partner with cross-functional stakeholders, and mentor engineers while remaining hands-on.

Build and maintain reliable data pipelines, computed tables, and scalable data models using SparkSQL and PySpark. Integrate IoT, product, customer, external, unstructured, and ML-related data into a central data lake for analytics, causal inference, model training, and dashboards. Ensure high data quality and uptime while collaborating with data science, analytics, AI/ML, and engineering teams.

Leads enterprise data engineering strategy, architecture, modernization, and governance. Designs scalable cloud-native data lakes, warehouses, lakehouses, ingestion pipelines, and real-time processing platforms. Establishes standards for data modeling, security, quality, observability, DataOps, DevOps, and compliance. Provides technical leadership across engineering teams, mentors senior professionals, drives transformation programs, and partners with business and technology stakeholders to deliver resilient data products supporting analytics, regulatory reporting, and operational intelligence.

Build and manage large-scale automotive data infrastructure, telemetry pipelines, analytical models, dashboards, and AI/ML solutions. Analyze battery, vehicle electronics, warranty, and diagnostic data to identify patterns, resolve issues, and support vehicle program decisions. Architect Azure Databricks lakehouse solutions, develop diagnostic and prognostic algorithms, automate root-cause analysis, and maintain production software and reporting systems.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.