Top Data & Analytics Jobs

The role involves designing and scaling data infrastructures for private equity, developing ETL/ELT pipelines, ensuring data quality, and collaborating with various teams on data governance.

The Data Engineer will develop secure, scalable data pipelines for IRS applications, supporting complex analytics and machine learning to combat fraud and ensure compliance. Responsibilities include pipeline engineering, troubleshooting, and collaboration with data teams to create production-ready analytics solutions.

The DMS Data Engineer is responsible for analyzing raw data, building data systems and pipelines, conducting complex data analyses, and collaborating on projects to enhance data quality and reliability.

The Data Engineer will design and develop data pipelines for quality monitoring, assess data integrity, and build web applications supporting user validation and analysis.

Build and maintain ETL/ELT pipelines in a lakehouse/cloud environment, ensure data quality and lineage, and apply classical statistics and ML (anomaly detection, regression, time-series) to support operational decision-making and stakeholder reporting.

Designs, builds, and maintains scalable batch and streaming data pipelines, ETL/ELT workflows, data models, and Python transformations. Writes optimized SQL, monitors pipeline reliability, troubleshoots failures, implements data-quality validation, and supports security and governance practices. Collaborates with engineers, analysts, stakeholders, and clients to define data contracts, improve platforms, document lineage, and deliver reliable analytics data.

Administer, maintain, and optimize SQL and NoSQL database environments; apply data warehousing concepts; perform data management and maintenance using SQL, Python, and Apache Spark; support cloud database infrastructure with Git, AWS, or Azure; and provide database support for Tableau and Power BI reporting.

Administer, maintain, and optimize SQL and NoSQL database environments. Apply data warehousing and data management practices, using SQL, Python, and Apache Spark. Support database infrastructure with Git and AWS or Azure, and provide database support for Tableau and Power BI reporting and visualization tools.

Design, build, and maintain large-scale data pipelines and DAGs (Airflow) that process imagery, LiDAR, and other sensor data. Improve platform reliability, observability, resource tuning, and Kubernetes deployments. Collaborate with ML, hardware, and full-stack teams; own pipeline design, implementation, testing, and operations in a hybrid NYC role.

The Cloud Data Engineer designs and supports scalable Snowflake and Azure data architectures, environments, and ETL/ELT pipelines. Responsibilities include Snowflake performance and cost optimization, Azure resource management, security controls, troubleshooting, documentation, design reviews, and collaboration with architecture, engineering, BI, and security teams. The role also contributes to software development, testing, debugging, mentoring, and data warehouse and data mart implementation.

Design, build, maintain, and optimize Spark and Scala ETL/ELT pipelines on AWS for terabyte- and petabyte-scale datasets. Work across S3, Snowflake, Kafka, and monitoring systems, resolve production data-quality and performance issues, write automated tests, participate in architecture and code reviews, and support on-call operations. Use generative AI to improve pipeline development, monitoring, and engineering productivity.

Designs, develops, tests, deploys, and supports batch and streaming data pipelines using Azure and Databricks. Builds ETL/ELT processes, optimizes data workflows, supports cloud modernization, and performs monitoring, troubleshooting, and root cause analysis. Collaborates with business and technical teams on requirements, data governance, lineage, and audits. Mentors junior engineers and communicates solutions to technical and non-technical stakeholders.

Build and maintain scalable production data pipelines, infrastructure, warehouse models, analytics platforms, and data-quality systems. Partner with software engineers to instrument product features and ensure reliable event data. Support Hex and Snowflake self-serve analytics, maintain business metrics and data logic, and develop monitoring, validation, alerting, and observability tooling. The role also collaborates with product, analytics, marketing, and engineering teams, with potential future exposure to recommendation and LLM-based product work.

Build scalable data processing applications, ETL/ELT pipelines, AWS-based data solutions, and analytics integrations. The role involves optimizing high-throughput systems, supporting engineering analysis and optimization, working with structured and unstructured data, and collaborating across engineering teams. Candidates need a bachelor’s degree in engineering, U.S. citizenship, at least two years of software engineering experience, and proficiency in C# and Python.

Lead architecture and standards for CoreWeave's enterprise data ecosystem: design lakehouse/streamhouse architectures, modeling and semantic layers, governance, data quality, lineage, observability, and reusable data products. Drive cross-domain solutions, resolve performance and scalability constraints, evaluate core data technologies, conduct architecture reviews, and mentor senior engineers.

Lead architecture and hands-on engineering to design, scale, and govern a cloud-native lakehouse using Databricks and Microsoft Fabric. Build batch/streaming pipelines, ensure HIPAA-compliant PHI handling, produce analytics-ready datasets, mentor engineers, and drive platform strategy, governance, and reliability.

Responsible for building data models, developing ELT pipelines, implementing data quality checks, and utilizing best practices in data engineering and healthcare analytics.

Designs, builds, and maintains secure cloud data pipelines and centralized repositories; publishes curated datasets; develops WPS AI agents; produces dashboards and visualizations for senior military leadership; supports NATO and annual statutory reporting and prepares technical briefings and deliverables.

Build and maintain ingestion pipelines for heterogeneous healthcare data (TPA feeds, claims, eligibility, enrollment). Design dbt models and orchestration (Dagster/Airflow), implement data quality monitoring and observability, and partner with data science to produce and maintain feature pipelines and feedback loops.

Design, build, and maintain ETL pipelines, databases, and data models to process healthcare claims and related data. Ensure data quality, analyze claims data for trends, and produce reports and visualizations for nontechnical stakeholders. Collaborate with policy and analytics teams to translate requirements into data solutions and improve data infrastructure and analytics capabilities.

Lead hands-on data engineering and architecture for a classified on-prem Analytics Core: build ETL/ELT pipelines, clean and model 30 years of multi-modal and geospatial data, integrate ESRI spatial data, establish AI/ML feature standards, enforce DoD-aligned governance, and mentor transitioning staff to operationalize the solution.

The Data Engineer will design and implement data ecosystems, build data pipelines, and ensure data quality while collaborating with stakeholders and utilizing AWS services.

Design and implement end-to-end data solutions on Microsoft Fabric and Azure for insurance datasets. Build ingestion pipelines (batch, streaming, API), Lakehouse architectures, and data models. Enforce governance, security, compliance, and cost/performance optimizations while guiding engineers and stakeholders on architectural best practices.

Leads end-to-end data engineering solutions across multiple client engagements, from discovery and architecture through production support. Designs and optimizes Snowflake data platforms, models, warehouses, ELT/ETL pipelines, APIs, integrations, and deployments. Provides technical leadership, client communication, estimation, governance, performance and cost management, engineering standards, mentoring, training, and production escalation support.

Build and maintain data pipelines, warehouses, transformation workflows, data models, and reporting layers. Improve data quality, governance, reliability, performance, and cost efficiency across analytics systems. Manage ingestion and event-streaming workflows, develop star-schema data marts, and collaborate with analysts and stakeholders to translate business needs into scalable data solutions.