Top Data & Analytics Jobs

Lead design and implement secure, scalable data ingestion, curation, and semantic/ontology layers. Build production-quality code and pipelines (Databricks/Cloud), apply agentic AI frameworks, produce architecture artifacts, guide teams, and promote engineering best practices and CI/CD.

The Principal Data Engineer will lead data engineering efforts on public cloud, design scalable solutions, build data pipelines, and drive deliverables with a high level of technical expertise in relevant technologies.

The Solutions Data Engineer will manage data integrations end-to-end, utilizing FloQast's Data Platform and Data Studio to connect source systems and transform data for reliable outputs across applications.

Design, build, and maintain scalable ETL/data pipelines for Comcast's national locates program. Ingest and transform data from multiple internal and external sources, optimize performance and cost, ensure data quality and governance, collaborate with analysts and stakeholders, troubleshoot pipelines, and document data structures and processes.

Design, build, and maintain scalable batch and real-time data pipelines on an Azure data platform using Azure Data Factory, Databricks, and PySpark. Implement Medallion architecture (Bronze/Silver/Gold), enforce data quality and governance, optimize Spark jobs and cluster usage, monitor pipeline health, and collaborate with data science and reporting teams. Document lineage, handle CDC/incremental loads, and integrate streaming ingestion and validation checks to ensure reliable analytics and ML-ready datasets.

Design, build, and maintain data architectures and pipelines to ensure data quality, lineage, and accessibility. Implement ingestion frameworks and transformations across cloud and on-prem platforms, enable data consumption (APIs, views, extracts), monitor data observability, and collaborate cross-functionally to meet privacy and business requirements. Work may include nights/weekends and requires independent technical judgment.

Lead design, implementation, and maintenance of production-scale AWS data services and pipelines (batch & streaming). Drive data ingestion, modeling, warehousing, lakes, APIs, and governance; coordinate S3, SQS, Lambda, DynamoDB; collaborate with product and analytics teams; ensure reliability, scalability, and compliance in defense-related analytics solutions.

Design, build, and maintain large-scale data pipelines and lake architectures on AWS using Spark/Databricks. Ensure data quality, lineage, observability, and TDD-based testing. Support self-service analytics, semantic layers, and cross-functional stakeholders while providing production support and documentation.

Design, build, and maintain production data pipelines and curated Databricks datasets to support reporting, analytics, and decision-making. Partner with analysts and stakeholders to translate planning, portfolio, workforce, finance, and actuals needs into reusable data products. Improve data trust via quality checks, lineage, metadata, monitoring, and automation; support scenario modeling, utilization analysis, and interactive reporting to replace manual spreadsheet workflows.

Develop and maintain Python data pipelines to produce business data objects supporting applications. Build and deploy CI/CD pipelines, implement source-to-target mapping as pipeline code, and perform unit and integration testing. Solve complex application problems, interpret design requirements, uphold software quality standards, and mentor team members. Work onsite in Chicago or Peoria, IL.

Design, build, and operate big-data and streaming platforms (Kafka, Hadoop, Dremio). Develop, deploy, and monitor data pipelines using Java, Python, Spark/Flink; collaborate on data modeling and ingestion, ensure data integrity and availability, act as a Big Data SME, and perform root-cause analysis.

Entry-level Junior Data Engineer to help build and maintain cloud data pipelines into BigQuery using tools like Fivetran and Python. Contribute to Medallion (bronze/silver/gold) layers, data transformations (dbt), basic data quality checks, CI/CD practices, documentation, and governance while learning cloud engineering, orchestration, and observability under senior engineers.

Design, develop, and maintain end-to-end data pipelines and data products for analytics. Troubleshoot complex data issues, document data sources, onboard backend and 3rd-party data, mentor peers, evaluate and deploy new tools, and collaborate with business partners to enable data-driven decisions.

Design, build, and maintain scalable data systems and pipelines (batch and streaming). Implement data formats, modeling, and infrastructure; support deployment automation, stakeholder requirements, and data governance to enable analytics and reporting.

Develop and maintain Python data pipelines and CI/CD deployments to build business data objects. Implement source-to-target mappings, unit and integration tests, resolve complex technical issues, and uphold software quality and best practices within the team.

Design, build, and maintain a Microsoft Fabric Lakehouse and Azure Synapse data warehouse. Develop and optimize ETL/ELT pipelines, dimensional/semantic models, and data quality checks. Tune SQL and Spark workloads, implement monitoring/alerting, handle production incidents, and integrate source systems and APIs. Partner with stakeholders, follow Agile practices, document runbooks, and participate in code reviews.

Lead design, implementation, and operationalization of scalable analytics pipelines and BI solutions on cloud platforms (Databricks). Drive data quality, lineage, security, governance, performance tuning, and AI-enabled insights. Partner with product, engineering, and business stakeholders and mentor analytics engineers across global delivery models.

The Senior Data Engineer will design and execute data management strategies, build cloud solutions, develop data pipelines, and ensure data quality and integrity.

Design, build, and maintain large-scale ETL pipelines and data warehouses (Redshift) with strong data modeling, governance, and quality controls. Optimize systems for performance and reliability, integrate and deploy AI/ML models, troubleshoot and support Redshift, collaborate with data scientists and stakeholders, and produce technical documentation and knowledge-base articles.

Design, build, test, and maintain secure, scalable data pipelines and architectures. Work with Python, Databricks, SQL/NoSQL, and enterprise AI capabilities to deliver trusted data collection, storage, access, and analytics. Validate AI-assisted outputs, update data models, customize tools, and ensure controls, traceability, and data sensitivity handling.

The Principal Data Engineer will lead the technical strategy for data initiatives, focusing on real-time data platforms and collaboration with stakeholders to design efficient and innovative data architectures, ensuring data governance and compliance.

As a Data Engineer, you will design, implement, and maintain data systems for Notifications at Whatnot, ensuring data quality and performance while collaborating with cross-functional teams on data architecture and workflows.

Design, build, and maintain scalable data pipelines and API integrations; implement data quality monitoring and alerting; troubleshoot data issues; collaborate with business stakeholders; support CI/CD processes and scale architecture for future needs.

As a Data Engineer at PNC, you will design, build, and maintain robust OLTP databases, ensuring high availability and strong performance while collaborating with cross-functional teams to manage data integrity and security.

Summer internship focused on building scalable data architectures and pipelines, automating data processes, improving data quality frameworks, and supporting cloud infrastructure. Work includes AWS automation, data warehousing, workflow optimization, platform integration, and operationalizing tools to process structured and unstructured data while leveraging analytics, AI/ML, and standardized pipelines.