Data Engineer
Design, build, and maintain scalable ETL/ELT data pipelines to ingest data from various internal and external sources. Collaborate with stakeholders to ensure data integrity, security, and accessibility for analysis and model training.
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Design, build, and maintain scalable ETL/ELT data pipelines to ingest data from various internal and external sources. Collaborate with stakeholders to ensure data integrity, security, and accessibility for analysis and model training.
Design, build, and maintain robust batch and streaming data pipelines while ensuring data quality and observability. Collaborate with cross-functional teams to transform ambiguous requirements into dependable data products.
Design, build, and maintain robust data pipelines and engineering workflows using SQL, Python, and Databricks. Support critical initiatives including data ingestion, process automation, transformation, and data quality assurance.
The Data Engineer will design, develop, and maintain scalable ETL pipelines and data storage solutions to support client data needs. They will collaborate with cross-functional teams to ensure high-quality data delivery while adhering to Agile development practices.
Design, develop, and maintain scalable data pipelines and ingestion strategies using Snowflake, Python, and PySpark. Collaborate with cross-functional teams to implement data quality rules, governance, and security across all storage layers.
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Design and maintain scalable AWS data platforms and pipelines for batch and streaming workloads. Implement data governance, normalization workflows, and knowledge graphs to support analytics and AI/ML use cases.
The Data Engineer will design, build, and optimize ETL/ELT workflows and develop scalable data pipelines. They will also collaborate with cross-functional teams to deliver impactful data solutions.
You will architect, design, and implement scalable data engineering solutions to transform data from disparate systems into actionable insights. You will collaborate with cross-functional teams including data scientists and analysts to support business decision-making using Agile methodologies.
Design, develop, and maintain scalable data pipelines using Microsoft Azure Fabric while building reliable ETL/ELT processes. Collaborate with stakeholders to translate business requirements into technical solutions and optimize workflows for performance and cost.
Design, build, and deploy end-to-end cloud data solutions that are secure, scalable, and performant for enterprise clients. Collaborate with cross-functional teams and client stakeholders to deliver high-quality data architectures and integration patterns.
Develop and maintain scalable batch and streaming data pipelines using Python, PySpark, and Azure Databricks. Support machine learning projects, MLOps workflows, and model deployment while collaborating with cross-functional teams.
The Senior Data Foundry Engineer is responsible for building and scaling user-facing data applications and custom widgets to support the digitization of the Compliance Organization. This role involves managing the full lifecycle of data from ingestion and transformation to ontology design and application deployment.
The Data Engineer will collect, process, and clean data from various sources to build predictive models and actionable reports. They will also design ETL pipelines and automation workflows to help clients make data-driven decisions.
You will build and maintain scalable data pipelines and transformation models to support warehouse intelligence. Additionally, you will define consistent metrics and ensure data quality through rigorous testing and lineage tracking.
You will own the data architecture across all GTM applications, including schema design, object modeling, and the implementation of a canonical data dictionary. Additionally, you will bridge R&D and production systems, ensuring scalability, reliability, and security while mentoring an AI-native engineering team.
Design, build, and optimize scalable ETL/ELT pipelines and reusable data engineering components using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Develop and operationalize agentic workflows for data validation, troubleshooting, and automation, while implementing production controls and supporting deployment, monitoring, and ongoing improvements.
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You will own the development and operation of scalable web-scraping and data ingestion pipelines within a lakehouse architecture. This involves designing robust frameworks for extraction, monitoring, and failure handling while ensuring compliance with legal and privacy standards.
Design, build, and operate core components of the data platform, including streaming ingestion and MPP query engines. Ensure operational excellence, reliability, and performance tuning while mentoring junior team members.
Design, create, and maintain optimal data pipeline architectures within the Palantir Foundry environment. Monitor data pipeline health, configure alerts, and collaborate with business leaders to translate requirements into data-centric solutions.
The Associate Data Engineer is responsible for designing, developing, and maintaining data pipelines and warehouses to support business intelligence reporting. They will manage ETL jobs, validate data models, and collaborate with stakeholders to ensure accurate data delivery.
Design and build services that power machine learning products while providing infrastructure and automation for data scientists. Maintain and improve production data systems while collaborating with cross-functional teams through code reviews.
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Architect and own the end-to-end extraction and ETL pipeline to transform unstructured web data into a high-accuracy B2B dataset. Define architectural standards for extractor frameworks, LLM infrastructure, and agentic workflows while ensuring cost-effective and reliable data processing.
Design, create, and maintain optimal data pipeline architectures within the Palantir Foundry platform. Collaborate with business leaders to translate information requirements into data-centric solutions and monitor pipeline health.
Architect and implement scalable data pipelines while developing ETL/ELT processes to ingest data from various sources. Collaborate with cross-functional teams to perform exploratory data analysis and design data models that support high-quality analytics.
Architect and implement scalable data pipelines while developing ETL/ELT processes to ingest data from various sources. Collaborate with cross-functional teams to perform exploratory data analysis and design data models that support high-quality analytics.
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