Top Data & Analytics Jobs

As a Palantir Data Engineer, you'll work on data exploration and analytics projects, apply innovative methodologies, and support military missions using AI solutions.

As a Data Engineer, you will develop data-driven decision analysis products for the DoD, ensuring effective stakeholder engagement, and leveraging advanced analytics and machine learning techniques.

The Data Engineer will develop data-driven decision analysis products, provide analytic consulting, support stakeholders with tools, and optimize defense systems for national security.

The Data Engineer will build and maintain scalable data pipelines, ensure data quality, and support a metadata-driven framework while collaborating with stakeholders.

Lead design, build, and operate real-time, Flink-based streaming data platforms. Develop low-latency, stateful pipelines, enforce event schemas/contracts, ensure fault tolerance and observability, mentor engineers, and partner with architecture and compliance teams to deliver resilient streaming solutions for banking use cases.

Design, build, and optimize scalable data pipelines and ETL workflows using Azure and PySpark. Ensure data quality, performance, and system reliability; support data governance, metadata management, SQL Server administration, and Power BI reporting while collaborating with business partners in an Agile environment.

Lead graph data engineering to design, implement, and scale Neo4j-based graph platforms. Build graph models, ingestion pipelines (batch and streaming), integrate graphs with analytics/ML, establish governance, and enable graph-driven business use cases like fraud detection and Customer 360.

Operate and own real-time, petabyte-scale streaming pipelines (Pub/Sub/Kafka, Dataflow/Beam), integrate LLMs/Vertex AI enrichment, build monitoring/alerting, respond to incidents, ensure data integrity, and collaborate with Product and Analysts to improve observability and reliability.

Design, build, and operate enterprise-scale data pipelines and platforms (ETL/ELT) using Palantir Foundry and Databricks. Parse and process structured and unstructured data, develop scripts to convert data, mentor cross-functional teams, and monitor/scale data solutions for mission-driven analytics.

Design, build, and maintain scalable ETL/ELT pipelines and data platforms using distributed tools and cloud services. Mentor cross-functional teams, oversee data architecture and modeling, process structured and unstructured data, and support analytics and mission-driven projects while meeting security clearance requirements.

Design, build, and maintain scalable data stores, ETL/ELT pipelines, and data-processing software for structured and unstructured data. Collaborate with cross-functional teams to deploy, monitor, and operate data platforms and support analytics and machine learning workloads. Support assessment, design, and maintenance of scalable data solutions for mission-driven clients. Must be able to obtain Secret clearance and hold a bachelor’s degree.

Design, build, test, deploy, and monitor batch and streaming data pipelines and ETL processes. Evaluate and adopt data technologies (including Adobe Experience Platform and CDPs), collaborate with data scientists, architects, and business partners to ingest and prepare large semi-structured/unstructured datasets for ML training, model execution, and search analytics, and implement automation, documentation, and tooling for scalable analytic data systems.

Design, build, and operate Databricks Lakehouse pipelines (Bronze/Silver/Gold) using Delta Lake and DLT. Optimize Spark/SQL workloads and Delta tables for performance and cost, implement incremental processing with exactly-once semantics, ensure data quality, and deliver documented datasets and dashboards (Power BI/other) with SLAs. Collaborate with cross-functional teams, integrate real-time sensor data, and support stakeholders. Occasional travel; primarily on-site.

Lead architecture and implementation of a large-scale analytics stack: design foundational pipelines, establish modeling/orchestration standards, integrate AI-assisted engineering tools, translate business questions into version-controlled data models, partner with stakeholders to define metrics and data contracts, migrate logic into maintainable pipelines, and optimize cloud warehouse performance for scale.

Build and maintain ETL/ELT pipelines, design data models, and develop data workflows using Snowflake, dbt, Python, and SQL. Integrate data sources with tools like Airbyte/Fivetran/Dagster, leverage AWS services (S3, Lambda, API Gateway), use Git for version control, and optionally support Tableau visualizations. Remote role with 3–7 years experience.

Lead design and implementation of Azure cloud Lakehouse and ETL/ELT solutions using Databricks and PySpark. Architect medallion Delta Lake platforms, build metadata/YAML-driven pipelines, optimize Spark workloads, implement CI/CD and IaC, enforce data governance and quality, mentor engineering teams, and drive cloud migration and stakeholder alignment for enterprise analytics and downstream applications.

Lead data engineering engagements to design, build, and maintain ETL/ELT pipelines and cloud data architectures. Manage client accounts and mentor teams, leverage tools like DataStage, AWS/Redshift, DB2/SQL Server, GoldenGate, and BI/visualization platforms to deliver analytics, performance tuning, and scalable reporting solutions.

Design, build, and optimize Snowflake-based data warehouses and dbt transformation pipelines. Develop scalable ETL/ELT ingestion, tune SQL and warehouse performance, implement data quality/testing (dbt tests), and maintain CI/CD and version-controlled data workflows while collaborating with analysts, data scientists, and stakeholders.

The Lead Data Engineer delivers end-to-end data solutions, mentoring engineers, designing data architecture, and enhancing data pipelines across the organization.

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.

Design and implement scalable data engineering, ETL, data lake and reporting solutions. Build data infrastructure and pipelines using Oracle, Snowflake, Airflow, Informatica and AWS. Collaborate with product owners and analysts, ensure alignment with enterprise data strategy, perform data modeling, reconciliation, automation (Control‑M), and coach team members while participating in SDLC and DevSecOps practices.

Design, develop, test, deploy, and maintain scalable ETL and BI/data warehouse solutions in cloud/on-prem environments. Build CI/CD pipelines, automation, test frameworks, and data quality processes. Collaborate with analysts and engineers in Agile teams to deliver reliable, observable, enterprise-grade data platforms.

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.

Lead design, develop, test, implement and support cloud-based data publication and transformation solutions. Collaborate across Agile teams, work with distributed systems, streaming, data warehousing (Redshift/Snowflake), RDBMS/NoSQL, mentor engineers, and ensure code quality and performance.