Top Data & Analytics Jobs

The Senior Data Analyst will manage the lifecycle of data analysis projects for RevolutionParts, develop complex models using SQL and Python/R, ensure data integrity, create dashboards in Looker, and mentor the team on data strategy.

The Senior Data Engineer II will build and optimize data solutions on Axon's Corporate Data Platform, design data pipelines, mentor team members, and uphold data governance and quality standards.

Develop and implement a data management strategy, oversee data governance and architecture, ensure compliance, and manage vendor relationships to support organizational goals.

Design, build, and optimize scalable data ingestion and transformation pipelines in a medallion (Bronze/Silver/Gold) architecture on Microsoft Fabric/OneLake. Ensure HIPAA-compliant, multi-tenant data isolation, data quality monitoring, lineage and governance. Partner with Data Science, ML Engineering, and Software Engineering to deliver feature stores, ML-ready datasets, and production-grade data delivery for analytics and scoring.

Lead design, development, and optimization of cloud-native ETL/ELT pipelines and data lake architectures on AWS. Build scalable ingestion and streaming pipelines using Talend Cloud, PySpark, and Python microservices; leverage S3, Glue, EMR, Lambda, and Athena. Ensure data quality, governance, performance tuning, and operational monitoring; troubleshoot production issues, collaborate with stakeholders, and mentor junior engineers.

Design, build, test, and maintain high-quality data pipelines and storage systems. Collaborate with architects, analysts, scientists to ingest, transform, and prepare data for analytics and ML, implement DevOps best practices, write tests and documentation, and improve data reliability, performance, and security using modern cloud and big-data technologies.

Design, build, and scale reliable batch and real-time data pipelines and warehouses. Partner with engineering, data science, and business teams to define data models, improve observability, optimize storage and querying, and evangelize data engineering best practices across the company.

Lead end-to-end analytics solutions: gather requirements with stakeholders, build production-quality ETL and visualizations, coach junior analysts, communicate progress and roadblocks, support proposals and pitches, and contribute to firm initiatives and client delivery.

Design, build, and maintain scalable, fault-tolerant streaming data architectures and real-time pipelines using Kafka, MSK, and Kinesis. Implement stream processing, monitoring, security, IaC, and CI/CD. Collaborate with data teams, provide technical leadership, and optimize latency, throughput, and cost for real-time analytics and downstream systems.

Lead design and implementation of scalable data architectures and pipelines for real-time and batch processing; mentor junior engineers; ensure pipeline reliability, monitoring, security, and governance; collaborate with cross-functional teams and adopt new data technologies.

Lead the data engineering team to design, build, and scale data platforms and pipelines. Set standards, manage projects and technical debt, collaborate with stakeholders, drive adoption of new tools, ensure data governance, and provide technical leadership for complex data issues and real-time architectures.

Lead data engineering for a healthcare scheduling platform rebuild: map legacy multi-TB schema, design target schema, build translation layer (views/materialized views and write mappers), ensure data consistency during parallel systems, plan per-tenant cutover with verification and rollback, tune DB performance, instrument data quality/lineage/audit, and mentor engineers.

Build and maintain a near real-time financial dashboard by integrating market and forecast APIs, automating financial models, and crafting clear visual narratives using Flourish and Canva. Collaborate with design and marketing partners, ensure data accuracy and performance, and optimize data flows across multiple feeds.

Design, build, and maintain scalable ELT/ETL data pipelines and data models on cloud platforms (preferably Snowflake). Enable batch and near-real-time integrations, ensure data quality/monitoring, create APIs and production-ready datasets for BI, analytics, and ML, optimize performance and cost, document architectures, evaluate new technologies, and mentor engineers.

The Senior Data Engineer will architect event-driven data pipelines, automate ETL processes, and ensure operational excellence in NVIDIA's financial infrastructure.

The Staff Data Engineer will lead the architecture and reliability of Hinge Health's data platform, manage technical complexity across teams, set data standards, and enhance engineering practices.

As a Senior Staff Data Engineer, you'll set the technical vision for data architecture, lead initiatives across teams, mentor engineers, and ensure platform reliability, while integrating data systems with ML pipelines effectively.

Design and build scalable batch and streaming data pipelines, lead architecture for Lakehouse platforms, and mentor junior engineers while optimizing data quality and performance.

Design and deliver scalable data solutions for analytics and reporting, involving hands-on data engineering and technical leadership, while ensuring quality and governance.

As a Staff Data Engineer, lead significant data projects, ensuring scalable architectures and optimized data pipelines, while mentoring others and collaborating cross-functionally.

The Senior Data Engineer will design, build and deploy data solutions for AI technologies, mentor junior engineers, and collaborate across teams.

Design, implement, and maintain large-scale data pipelines and storage. Build ETL processes, integrate data sources, optimize big-data workflows using Hadoop/Spark/Kafka, manage data lakes/warehouses, implement and operate Elasticsearch clusters, and collaborate with data scientists to support analytics and continuous improvement.

The Lead Data Engineer modernizes and optimizes the Medicaid Market's data platform, manages ETL processes, and partners with the Business Intelligence team to enhance data accessibility and reliability, while also leading contract resources in a complex environment.

Design, build, and maintain data collection, storage, and full‑stack applications for manufacturing and test data. Implement CI/CD, APIs, databases, and deployment architecture; support internal users and test teams; mentor junior developers and follow software best practices.

Design, develop, and maintain large-scale data pipelines and ETL processes. Implement and optimize data storage and processing on BigQuery/GCP, ensure data quality and monitoring, participate in code reviews, mentor junior engineers, and help launch data-driven tools.