Top Data & Analytics Jobs

Develop and manage scalable ETL/ELT pipelines and data warehouses, ensuring performance, reliability, integrity, and consistency. Monitor systems, troubleshoot issues, collaborate with technical and business teams, document architectures, and improve data infrastructure for scalability. The role requires production-quality Python, AWS experience, strong debugging skills, and approximately four years of fintech or startup experience. It is based in Palo Alto with three days per week in the office.

Designs, builds, and maintains scalable Databricks and PySpark data pipelines; develops Python and SQL transformations; identifies insights from ambiguous datasets; and ensures data quality, governance, documentation, and reliable delivery. Collaborates with stakeholders and cross-functional teams to integrate data into applications and dashboards, troubleshoot pipeline issues, and support a Department of Defense Navy client.

Develop and operationalize data applications using Palantir Foundry for national security clients. Translate stakeholder needs into technical solutions, ingest and transform data, build workflows and applications, support scalable data platforms, and lead analytics projects. The role also involves advising clients on data-driven mission impacts, collaborating with Agile teams, and applying DevSecOps, AI/ML integration, security, and compliance practices.

Builds and maintains scalable batch and streaming data pipelines supporting analytics, machine learning, and business operations. Responsibilities include writing Python, SQL, and Spark code; ensuring data quality and reliability; monitoring and troubleshooting pipelines; implementing testing and observability practices; documenting data architecture; and collaborating with analysts, data scientists, product managers, and engineering teams in an agile environment.

Build and maintain data pipelines in Azure Synapse using Spark, Python, SQL, and C#. Apply data warehouse and lakehouse modeling standards, collaborate with analysts and testing teams, troubleshoot data issues, and develop machine learning and regression analysis capabilities using Azure ML and related tools.

Build and operate Pivotal’s flight data warehouse and telemetry pipelines using AWS Glue, Athena, SQL, and Python. Design curated data marts, fleet health alerting services, data quality gates, dashboards, and query interfaces. Own data infrastructure, deployment, monitoring, and schema evolution end to end while collaborating with firmware, GNC, battery, flight operations, systems engineering, and MRO teams on telemetry and airworthiness decisions.

Build and operate core data infrastructure for edge and hybrid environments, including ingestion, OT/IoT integrations, real-time streaming, data contracts, schemas, validation, lineage, observability, telemetry, and scalable query access. The role requires production ownership, reliability practices, customer integration, and collaboration across engineering and customer-facing teams. Candidates should have 6+ years of experience with production data, streaming, or integration systems and proficiency in Python and/or Go.

Develop and maintain Python and SQL processes for acquiring, transforming, normalizing, and loading large spatial datasets into databases. Evaluate commercial and open-source data quality, interpret product specifications, update ETL workflows, and support continuous geocoding data quality improvements. The role collaborates with product teams and manages multiple priorities in a fast-paced environment.

Build and own Stuut’s data infrastructure, including ingestion pipelines, canonical data models, semantic layers, event pipelines, quality testing, observability, and DataOps practices. Partner with product, engineering, and ML teams to deliver reliable datasets, dashboards, KPIs, and insights. This foundational role will establish the company’s data architecture and scale it as customer adoption grows.

Engineer and maintain mission-relevant data ingest and distribution pipelines across multiple networks. Configure ETL workflows, COTS/GOTS applications, databases, cloud systems, and CI/CD infrastructure. Troubleshoot production systems, hardware, protocols, and network connectivity while ensuring data reliability, low latency, and integrity. Collaborate with software developers, systems administrators, external providers, users, and stakeholders. Maintain system documentation, diagrams, monitoring, and operational workflows in an agile environment.

Architects, develops, and optimizes scalable data pipelines and cloud-native platforms. Leads data engineers in integrating structured and unstructured data across secure environments, establishes engineering standards, ensures data quality, and supports analytics, reporting, and mission-critical decisions. Uses ETL, distributed systems, streaming architectures, cloud services, and access controls to deliver reliable enterprise data infrastructure.

Builds and maintains reliable, scalable, secure data platforms and cloud infrastructure. Responsibilities include troubleshooting Tier 3 production issues, designing data ingestion and transformation workflows, implementing CI/CD, monitoring systems, and supporting enterprise data operations. The role may involve Kubernetes, Kafka, NiFi customization with Java, AWS services, and classified Intelligence Community environments.

Lead the architecture, development, governance, and operation of scalable data platforms, pipelines, schemas, databases, and warehousing infrastructure. Guide data acquisition, transformation, cataloging, and integration across cloud and local environments while ensuring reliability, security, performance, and data quality. Collaborate with product and engineering leadership, recommend data solutions, establish technical standards, and mentor engineering teams.

Build and operate a scalable data platform supporting pipelines, reporting, and analytics across business teams. Responsibilities include defining canonical data models, implementing workflow orchestration with tools such as Airflow, improving platform infrastructure, troubleshooting complex data and system issues, enforcing data security controls, and mentoring junior engineers. The role requires strong software engineering, cloud, infrastructure, communication, and data pipeline expertise.

Designs and maintains scalable infrastructure for ingesting, processing, storing, and serving large volumes of legal and financial document data. Responsibilities include building data pipelines and backend services, improving databases and schemas, implementing governance and GDPR compliance, supporting machine learning workloads, leading technical initiatives, and mentoring engineers.

Develops and operates integrated data systems supporting New Glenn certification and launch services. Responsibilities include ensuring data quality, maintenance, sharing, governance, customer engagement, training metrics, and certification support. The role collaborates with New Glenn teams, NASA, and U.S. Space Force partners; plans technical products, communicates results, enforces data-marking practices, manages resources, and provides product ownership in a fast-paced aerospace environment.

Build and scale analytical data products, models, semantic layers, and reusable metrics using dbt and Databricks. Develop Gold-layer and dimensional models, support Tableau analytics, automate data workflows with Python and SQL, and implement testing, documentation, lineage, Git, CI/CD, and deployment practices. Provide technical leadership, architecture guidance, mentorship, code reviews, performance optimization, and production support while partnering with business, product, analytics, and engineering teams.

Own NimbleRx’s data platform end to end, including ingestion, transformation, storage, querying, access, batch and streaming pipelines, warehouse modeling, security, compliance, and reliability. Optimize PostgreSQL and Java/Spring data-layer performance, investigate infrastructure issues, develop AI-enabled tooling, partner across teams, and mentor engineers and analysts.

Design, develop, and maintain data pipelines and software solutions to process and combine diverse data sources. Perform statistical analysis (regression, anomaly detection, clustering), automate and optimize data products, communicate findings to technical and non-technical stakeholders, elicit system requirements, validate performance, and support time-sensitive intelligence operations.

Own and evolve the enterprise data platform by architecting scalable data models, integrations, warehouses, and pipelines. Build and maintain solutions using Snowflake, dbt, Fivetran, Airflow, AWS, and Power BI. Integrate diverse data sources, implement monitoring and data quality frameworks, troubleshoot production issues, and deliver curated datasets for analytics and AI use cases. Provide technical leadership, establish engineering standards, and collaborate with business, governance, QA, and analytics teams.

Build and optimize batch and real-time ETL/ELT pipelines, data lakes, and cloud-based processing systems. Perform data ingestion, transformation, profiling, validation, governance, quality checks, and API integration across AWS and Azure environments. Write Python, JavaScript, and SQL; maintain technical documentation; support reporting and machine learning; mentor junior engineers; and ensure secure, reliable, compliant data operations.

Lead the architecture and technical direction of a cloud data platform spanning infrastructure, Databricks, CDC streaming, pipelines, data products, developer experience, and governance. Resolve cross-team technical challenges, establish platform standards, influence roadmaps, drive adoption, mentor senior engineers, and lead complex multi-squad initiatives with minimal direction.

Leads enterprise data platform architecture, engineering standards, automation, security, governance, observability, and cloud integration. Designs scalable batch and streaming pipelines connecting SAP, legacy systems, Databricks, and enterprise data products. Guides SAP transformation data harmonization, analytics and reporting strategy, infrastructure as code, CI/CD, reliability, and platform roadmaps. Provides hands-on technical leadership, mentors senior engineers, collaborates across business and transformation teams, and supports critical incidents.

As the first in-house Data Engineer, you will own the data infrastructure, build and maintain data pipelines, and collaborate with teams to ensure quality data access and insights.

Creates and manages data connections, validates and transforms client files, integrates external APIs and FTP feeds, and normalizes portfolio data into Clearwater’s standardized format. Monitors and automates daily processes, improves auto-reconciliation, resolves data issues, and implements operational enhancements. Communicates technical workflows to stakeholders, documents procedures, and provides light on-call support.