Top Data & Analytics Jobs

Lead a data engineering team to design, build, and operate secure, scalable data collection, storage, processing, and analytics platforms. Develop production-quality Python code, SQL transformations, orchestration (Airflow), and streaming pipelines (Kafka/PubSub). Champion AI-assisted engineering practices, automate remediation, evaluate vendor solutions, and produce reporting in JIRA/Confluence to improve delivery, resiliency, and security.

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.

Lead design and build scalable, low-latency, fault-tolerant streaming data platforms and distributed data pipelines. Own streaming analytics, cloud-based data stores, CI/CD practices, and mentor engineers while collaborating with product, security, and ML teams to deliver robust cloud solutions.

Lead design and optimization of high-throughput data ingestion pipelines and Python-based microservices. Improve pipeline performance, reliability, and observability across distributed systems, define unified ingestion architectures, work with Snowflake for analytics, mentor engineers, and collaborate cross-functionally to ensure scalable, fault-tolerant data platforms.

Designs and manages scalable data pipelines, contracts, transformation layers, and analytics models. The role focuses on data governance, observability, lineage, configuration validation, and collaboration with product engineering teams. It requires processing large datasets, integrating cloud and on-premises industrial technologies, and implementing streaming and message-queuing solutions, ideally in cybersecurity or industrial environments.

Architects and evolves enterprise-scale batch, real-time, and event-driven data platforms and pipelines. Leads data modeling, distributed processing, data quality, governance, observability, CI/CD, security, monitoring, incident response, and cost optimization. Provides technical direction across hybrid cloud environments, mentors engineers, leads critical initiatives and code reviews, manages technical debt, evaluates new technologies, and partners with business and technical stakeholders. Participates in on-call rotations and supports distributed and offshore engineering teams.

Build and operate Vercel’s data platform infrastructure, including streaming systems, storage layers, observability, access controls, and internal tooling. Work with Kafka, ClickHouse, Tinybird, and Snowflake to support reliable, scalable data movement and self-service data consumption. Contribute to platform features, fixes, compliance practices, and architecture decisions while developing greater independence through mentorship and code review.

Own reliable billing, usage, and contract data pipelines; build dbt transformations, data models, semantic datasets, and finance reporting infrastructure. Resolve data quality issues, establish testing and documentation standards, support revenue recognition and forecasting, partner with Finance stakeholders, make architecture decisions, review code, and mentor engineers.

Own reliable GTM data pipelines, dbt models, testing standards, documentation, and semantic datasets for pipeline, revenue, attribution, funnel, reporting, and forecasting metrics. Partner with Sales, Marketing, and RevOps leaders to translate business questions into durable data models, ensure data quality, set technical direction, review code, and mentor engineers.

Lead design, development, and support of scalable data solutions for counterparty credit risk. Build and maintain data pipelines, reports, and dashboards; collaborate with business stakeholders; resolve technical challenges; lead projects and mentor junior staff; ensure compliance with risk and regulatory requirements.

Design, build, and maintain scalable data solutions for enterprise counterparty credit risk. Lead and deliver moderately complex technical initiatives, collaborate with risk and business stakeholders, develop code/tests/documentation, support reporting and dashboards, and mentor junior staff.

Design, build, and maintain large-scale GCP data pipelines and data warehouse solutions using Dataproc, GCS, BigQuery, Hadoop/Spark/Hive and Airflow. Mentor engineers, run agile ceremonies, prioritize backlog, provide on-call support, and deliver data models and products at multi-TB/PB scale.

Design and manage financial systems for accurate cashback calculations, build and maintain reconciliation processes, ensure data integrity for yield-bearing instruments, collaborate with compliance, and partner with cross-functional teams to improve financial system efficiency.

Build and maintain data pipelines to ingest, normalize, and deliver messy healthcare data (FHIR, HL7, CCDAs, PDFs) into structured models for AI and clinical use. Implement ETL, real-time webhooks, document extraction, PII sanitization, storage on AWS, and monitor data quality while ensuring compliance with healthcare interoperability standards.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

Design, build, and optimize GCP-based data architectures and ETL/ELT pipelines using BigQuery, Dataflow, dbt, and orchestration tools. Ensure data quality, security, and integration from diverse sources; collaborate with stakeholders to deliver actionable insights.

Design, build, deploy, and operate production-grade data pipelines and products for healthcare and marketing datasets. Implement data models, Airflow workflows, data-quality controls, and PHI/PII privacy controls. Collaborate with product, analytics, and platform teams to support audience discovery, segmentation, activation, measurement, and reporting.

Build, deploy, and operate production-grade data pipelines and data products for healthcare audiences. Design transformations, data models, and governed views using Python, SQL, Airflow, S3, Snowflake, and EMR. Implement data-quality, monitoring, and privacy-by-design controls for PHI/PII. Partner with product, analytics, and platform teams to onboard sources, support audience discovery, segmentation, activation, measurement, and troubleshoot production issues.

As a Quantitative Developer, you'll innovate and enhance quantitative methods, develop custom tools, maintain data pipelines, and collaborate with researchers for advancing investment strategies.

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.

Develops data pipelines, transformations, data models, warehouses, and marts using cloud data technologies. Analyzes data quality, dependencies, integrations, and performance; writes, tests, documents, and deploys code; and supports production stability, SLAs, security, governance, and Agile delivery. Translates business requirements into technical data specifications, collaborates with upstream teams, troubleshoots data issues, and mentors less experienced programmers.

Support and improve enterprise data platforms by monitoring and troubleshooting ETL workflows, resolving production incidents, conducting root cause analysis, deploying enhancements, coordinating source-system changes with vendors and technical teams, tuning database and server performance, managing capacity, and maintaining operational documentation and stakeholder communications.

Design, implement, and optimize large-scale data processing on Databricks and Spark. Build ETL pipelines, integrate data across AWS services (Redshift, RDS), orchestrate workflows with Airflow, and ensure data warehousing and engineering best practices. Lead and mentor development teams and collaborate with cross-functional stakeholders to drive data strategy and execution.

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.