Top Data & Analytics Jobs

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.

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.

Design, build, and operate Databricks Lakehouse pipelines and Delta tables. Optimize Spark/SQL workloads, implement incremental/CDC patterns, ensure data quality, deliver documented datasets and dashboards (Power BI), and collaborate with stakeholders to meet SLAs while supporting data-driven decisions.

The candidate will improve the data ETL pipeline, build analytical tools, manage operations, and expand into new markets and asset classes.

Build and operate ingestion and transformation pipelines to turn messy insurance data into clean datasets. Design bronze/silver/fact layers, model and tune Postgres and analytical stores, define KPIs and reconciliation checks, orchestrate observable data jobs, and ensure end-to-end data quality for analytics and AI products.

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.

Design, build, and maintain scalable Databricks-based lakehouse pipelines (batch and near-real-time) on AWS, enforcing metadata, lineage, data quality, security, observability, and governance to support analytics and enterprise reporting.

Designs, builds, and maintains scalable batch and streaming data pipelines, data warehouses/lakes, and ETL/ELT processes. Collaborates with BI, Data Science, and Architecture teams to model data, validate sources, ensure data quality, optimize performance, and enable analytics using cloud and big-data technologies (AWS, Snowflake, Databricks, Spark, Kafka).

Build and own Dexory’s company-wide data foundation, integrating operational, product, commercial, contractual, and telemetry data. Define trusted data models, business metrics, SLAs, and KPIs; establish secure queryable datasets and AI-assisted natural-language access. Own pipeline health, data quality, architecture, and build-versus-buy decisions while partnering with engineering teams. As the founding data hire, set standards, deliver early value, and help recruit and lead the future data function.

The Data Engineer will design and manage data pipelines, develop datasets for product metrics, and collaborate with teams to ensure robust data integration and processing.

Design and implement SQL transformations and datasets, manage data models in Databricks, optimize performance, and ensure compliance with data governance standards.

Build and maintain HR data pipelines and warehousing (Snowflake), create data objects for Python and Tableau, use AI automation to clean large datasets, deliver actionable people analytics, consult with HR and executives, and manage multiple high-stakes analytics projects to inform workforce strategy and OKRs.

Lead architecture, systems, and roadmap for Coinbase's Compliance Reporting Data Platform. Design and build production-grade data models, pipelines, quality frameworks, validations, and certification processes. Establish data contracts, monitoring, reconciliation, and audit-ready processes. Partner with engineering and compliance stakeholders, respond to regulatory exams, and mentor engineers while shaping standards and documentation.

Build and maintain complex data pipelines in Azure Synapse using Spark, Python, SQL, and C#. Apply data warehouse and lakehouse modeling standards, support analytics and machine learning initiatives, ensure data quality, and collaborate with analysts. Mentor junior data engineers, review code, establish team guardrails, and guide execution while following software development lifecycle standards.

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.

Lead design and delivery of enterprise-grade Azure/Databricks data platforms for healthcare and commercial domains. Define architecture patterns, build scalable ingestion/transformation pipelines and reusable data products, implement governance and compliance, optimize performance and cost, and provide technical leadership and mentoring across end-to-end data engineering initiatives.

Lead Data Engineer designs, develops, tests, and supports cloud-based data solutions. Collaborates with Agile teams on distributed systems, streaming, data warehousing, and microservices, mentors engineers, and ensures code quality and performance.

Serve as a Data Steward and hands-on Oracle Database Developer to ensure data quality, governance, and security. Develop and optimize complex SQL/PLSQL, support data migration/ETL, troubleshoot performance, maintain database objects, and collaborate with stakeholders to define metadata, lineage, and data stewardship processes.

Develop data visualizations, interactive dashboards, and data pipelines to transform complex structured and unstructured data into actionable insights. Analyze data quality, types, structures, and relationships; use Python, R, or SQL for data manipulation and analysis. Collaborate with stakeholders to define reporting needs, support database-driven applications, and communicate findings. The role also involves mentoring team members, guiding algorithm and system development, and supporting defense and government clients.

Project-based intern supporting data analysis and process improvement for procurement and construction workflows. Responsibilities include cleaning and analyzing data with Python and spreadsheets, building dashboards and process tools, gathering stakeholder feedback, testing workflows with field and vendor teams, and presenting completed projects. Example initiatives include procurement cost analysis and redesigning mobile-friendly construction punch-list processes.

Design, build, and maintain ETL pipelines and big-data workflows from ingestion to consumption. Troubleshoot data/system issues, work in an agile team, handle structured/unstructured data and security logs, and support government data acquisition and sharing while meeting quality assurance and testing standards.