Top Data & Analytics Jobs

Build and maintain ETL/ELT pipelines to move product data into analytics-ready stores (Postgres, data lake, warehouse). Design and optimize data models, ensure data quality and documentation, support ad-hoc research requests, and collaborate with research and engineering teams to enable reproducible analytics.

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Build and maintain streaming, replication, and data platform pipelines using Kafka, CDC, and Materialize. Manage schemas, data contracts, infrastructure as code, clusters, access controls, CI/CD, monitoring, and observability across cloud environments. Participate in on-call rotations, incident response, design reviews, and technical documentation. The role requires a bachelor’s degree, five or more years of data engineering or backend engineering experience, and authorization to work in the United States without sponsorship.

Build and maintain streaming, replication, and data platform pipelines using Kafka, CDC, and Materialize. Manage schemas, data contracts, infrastructure as code, clusters, access controls, CI/CD, monitoring, and observability. Participate in on-call support, incident resolution, design reviews, and technical documentation. The role requires expertise in data engineering, streaming systems, Python, SQL, and cloud infrastructure.

Build and maintain streaming, CDC, and replication pipelines using Kafka and Materialize. Manage schemas, data contracts, infrastructure as code, clusters, access controls, CI/CD, monitoring, and observability across shared cloud environments. Participate in on-call rotations, incident response, design reviews, and RFCs, while maintaining documentation and runbooks. The role requires strong Kafka, streaming systems, Python, SQL, and cloud infrastructure expertise.

Design and develop AWS-based data platforms, build and maintain ETL pipelines using PySpark, perform data modeling and governance, integrate event-driven systems with Kafka, manage containerized deployments with Docker and Kubernetes, and produce clear documentation while collaborating with stakeholders.

Designs and maintains scalable enterprise data architectures and ETL/ELT pipelines on Microsoft Fabric and Azure. Responsibilities include developing Python-based data solutions, optimizing Fabric warehouses and lakehouses, implementing governance, security, lineage, monitoring, and CI/CD, and integrating diverse data sources. The role partners with data science, machine learning, business intelligence, engineering, and business teams to support analytics and model training while improving platform reliability, scalability, and cost efficiency.

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 operate scalable Databricks-on-AWS data pipelines using PySpark, Delta Lake, and lakehouse patterns. Optimize performance, implement data quality, monitoring, alerting, and automated remediation, and deliver curated datasets for BI and analytics partners. Collaborate with stakeholders on architecture and design while applying secure software engineering, CI/CD, agile, and operational stability practices. The role also uses AI-assisted development tools and supports workforce data analytics.

Own data pipelines into and out of the warehouse, dbt models, data integrity, governance, identity resolution, reverse ETL, and quality monitoring across growth, lifecycle, and brokerage data. Partner with product, marketing, and operations teams to establish data contracts, ensure accurate and timely business metrics, and support regulated financial data requirements.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. Collaborate with Agile teams, product managers, engineers, analysts, and data scientists while applying Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases. Establish reusable engineering patterns, data security controls, and resilient architectures. Mentor data professionals, communicate technical concepts to stakeholders, and deliver user-centric data solutions with minimal supervision.

Design, build, deploy, and support scalable cloud data platforms, pipelines, and applications for risk management. Lead technical direction across data engineering, analytics, and machine learning teams; establish reusable engineering patterns; implement security and privacy controls; and collaborate with product managers and software engineers. The role includes mentoring, stakeholder communication, distributed data processing, data modeling, warehousing, streaming, and both relational and NoSQL database solutions.

Design, build, and support scalable cloud data pipelines, platforms, and applications using Python, Spark, AWS, Databricks, Snowflake, and related technologies. Develop data models and end-to-end solutions across relational and NoSQL systems, enforce engineering patterns and security standards, and optimize reliability and performance. Collaborate with Agile teams, product managers, engineers, analysts, and data scientists while mentoring peers and communicating technical concepts to stakeholders.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Develop distributed data solutions using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Establish reusable engineering patterns, data security controls, and operational standards. Collaborate with product, engineering, analytics, and data science teams while mentoring others and communicating technical outcomes to stakeholders. Support resilient, high-performance data workloads and contribute to Agile delivery.

Design, build, and operate scalable cloud data pipelines, platforms, and applications. The role involves distributed data processing, data modeling, relational and NoSQL databases, data warehousing, security controls, observability, and real-time pipelines. The engineer collaborates with Agile teams, product managers, analysts, and software engineers; establishes reusable engineering patterns; communicates technical concepts; mentors peers; and independently delivers resilient data solutions.

Leads large-scale data engineering initiatives for Risk Tech, designing and delivering scalable cloud data platforms, pipelines, and applications. Responsibilities include architecture, data modeling, distributed processing, cloud warehousing, AI-enabled risk solutions, platform evaluation, operational resilience, and technical mentoring. The role collaborates with product managers, software engineers, analysts, and data scientists while independently driving architectural decisions and communicating technical outcomes to stakeholders.

Build and maintain healthcare data pipelines, including ingestion, transformation, aggregation, and warehousing. Own clinical terminology mapping, validation, reconciliation, data quality monitoring, and remediation. Optimize pipeline reliability and secure handling of sensitive health data while documenting lineage, quality rules, and operational procedures. Collaborate with providers, clinical analysts, and integration teams to resolve discrepancies and support a statewide Health Information Exchange and Health Data Utility.

Design and implement enterprise data platforms, lakehouse architectures, ingestion pipelines, knowledge indexes, and retrieval services for AI applications. Define data contracts, schemas, APIs, governance, permissions, lineage, and quality controls across structured and unstructured sources. Operate reliable data and retrieval services, troubleshoot failures, and assess relevance, latency, scalability, and freshness while partnering with AI Engineering. U.S. citizenship and eligibility for Public Trust or Suitability/Fitness determination are required.

Design and implement enterprise data platforms, pipelines, connectors, knowledge indexes, and retrieval services for AI applications. Integrate structured and unstructured data while maintaining permissions, lineage, quality, freshness, and reliability. Define data contracts, schemas, APIs, and governance patterns; troubleshoot ingestion and retrieval issues; and partner with AI engineers to improve retrieval behavior and response quality.

Design, build, optimize, and maintain scalable batch and real-time data pipelines, data models, schemas, and storage systems. Ensure data quality, reliability, performance, and secure delivery across platforms. Collaborate with data scientists and analysts, troubleshoot data systems, evaluate emerging technologies, and mentor junior engineers. The role is onsite in a SCIF supporting STRATCOM and requires Top Secret/SCI clearance.

Develop scalable, maintainable, and high-performing data pipelines and enterprise data lake solutions. Partner with stakeholders and technology teams to create analytics solutions, reports, and dashboards that improve patient outcomes, operational efficiency, and data-driven decision-making. Support data quality and enterprise data initiatives across the organization.

Build, deploy, and maintain enterprise-scale data applications, platforms, and ETL/ELT pipelines. Organize structured and unstructured data from disparate sources, develop scripts for data conversion, and support scalable, monitored data platforms. Collaborate with analysts, engineers, developers, and data consumers in an agile environment while contributing to mission-driven projects involving analytics, fraud detection, research, and national intelligence.

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.

Lead client data migrations into DealCloud, including requirements gathering, ETL, data mapping, transformation, loading, validation, reconciliation, QA, and go-live support. The role also supports platform configuration, technical solution design, project documentation, status reporting, escalations, and post-launch client assistance. Success requires strong data integration, analytical, communication, and prioritization skills, along with familiarity with SaaS, CRM, cloud platforms, APIs, and financial services workflows.

Build, maintain, and optimize cloud data pipelines using SQL, Python, Snowflake, BigQuery, and dbt. Integrate data from APIs, databases, cloud storage, marketing platforms, and third-party sources. Implement data quality checks, monitoring, troubleshooting, automation, and performance improvements. Collaborate with architects, analytics teams, and stakeholders to deliver reliable, reusable, analytics-ready datasets.