Top Data & Analytics Jobs

Design, build, and productionize batch and streaming ETL/ELT pipelines and ML workflows on Azure Databricks. Implement Unity Catalog data products, MLflow model lifecycle controls, CI/CD for data/ML artifacts, and operational monitoring. Partner with cross-functional teams to modernize legacy workflows into scalable, production-ready solutions.

Lead Data Engineer designs, develops, tests, and supports cloud-based, distributed data solutions. Collaborates across Agile teams, mentors engineers, works with big data, streaming, NoSQL and data warehouse technologies, and ensures performant, well-tested code for customer-facing products.

Lead Data Engineer designs, builds, and supports cloud-based data solutions for risk management. Collaborates across Agile teams to implement distributed, real-time, and ML-enabled data platforms using cloud, big data, and database technologies, mentors engineers, and ensures code quality and performance.

Design, build, test, and support cloud-based data platforms and real-time pipelines. Collaborate across Agile teams to deliver distributed microservices and data solutions using big data technologies, cloud data warehouses, streaming tools, and modern programming languages. Mentor peers, participate in code reviews, and ensure performant, well-tested systems that power customer experiences.

Design, build, and optimize scalable ELT pipelines into BigQuery, create dbt models and data models, implement data quality, observability, CI/CD, and IaC practices, and partner with stakeholders to deliver trusted enterprise data products.

Build and maintain ELT pipelines, data storage and quality frameworks, and observability for enterprise systems. Support AI/ML feature pipelines, ensure compliance with FDA/ITAR/DCAA, evaluate data platforms, and implement modern development practices (source control, CI/CD). Collaborate across functions to translate business requirements into production-grade data solutions.

Build and operate low-latency, real-time data and ETL pipelines for sports analytics products. Support production systems during live events, develop predictive analytics and APIs, and integrate complex real-time datasets into consumer and enterprise offerings.

Design, develop, and optimize cloud-based ETL/ELT pipelines and data architectures using Informatica and Databricks in AWS GovCloud. Author and optimize SQL and Python scripts, build secure RESTful APIs, perform data validation, migrations from on-premises to cloud, and troubleshoot performance issues across large datasets. Support DON Jupiter enterprise environment and related ETL frameworks.

Design and build data warehouse, lakehouse, and analytics solutions on Microsoft Fabric and Azure SQL. Implement Medallion Architecture, develop reliable ELT/ETL pipelines, model data for analytics, ensure data quality and monitoring, consult with clients on requirements and governance, and provide technical leadership and mentoring.

Maintain and monitor Snowflake data warehouse and ingestion pipelines. Troubleshoot file/load/data quality issues, operate SFTP/drop-zone/file automation, support scheduled loads, build dbt models and tables, document runbooks, and serve as backup for core warehouse operations while collaborating with engineering and IT.

Build, deploy, and maintain secure, scalable data pipelines and analytics on AWS/Databricks. Implement CI/CD, governance (Unity Catalog, lineage), platform observability, and operational support for enterprise datasets and Databricks SQL reporting.

Lead design and delivery of scalable data pipelines and APIs for Digital Risk Protection. Mentor engineers, align and ingest multiple data streams (including graph data), productize analytics and LLM outputs, and drive DRP technical projects with stakeholders to enable data-driven intelligence.

Design, build, and optimize relational database solutions and cloud-based data pipelines using SQL Server/Azure SQL, Databricks/Spark, Python/PySpark. Improve performance, support ETL/ELT, dimensional modeling, and AI/ML data needs. Collaborate with cross-functional teams, troubleshoot production issues, and adhere to governance and security standards.

Lead the design and development of scalable ETL pipelines, data integration solutions, warehouses, and data products. Build advanced SQL and Python processing systems, mentor engineers, collaborate across teams, and ensure data platforms meet business and compliance needs. The role requires architecting reliable enterprise data solutions and may involve strategic data planning.

Build and maintain data engineering foundations for Disney’s enterprise Data Unification initiative. Develop federated query layers, semantic data interfaces, knowledge graph and GraphQL capabilities, data pipelines, and integrations across heterogeneous sources. Collaborate with ontology, data science, and business teams to support semantic modeling, AI, and RAG use cases. Apply data modeling, query optimization, API development, DevOps, and cloud platform expertise while developing understanding of business processes.

Build and scale data pipelines supporting Vehicle Configuration Optimization. Ingest and transform Snowflake data into Databricks using Bronze, Silver, and Gold medallion layers. Develop Python and SQL ETL/ELT workflows, monitor pipeline health, optimize performance, and implement data quality and governance controls. Partner with data scientists, analysts, and stakeholders to produce curated datasets for analytics, simulation, and machine learning.

Data Engineer Interns will support analytics platform enhancements, data modeling, data exploration, pipeline maintenance, troubleshooting, optimization, documentation, and stakeholder collaboration. The role focuses on building data products, supporting AI-enabled analytics, and translating business needs into data requirements. Candidates should be enrolled in a quantitative degree program and have foundational knowledge of business intelligence, databases, data modeling, SQL, and data visualization.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

Lead Data Engineer/DB2 DBA responsible for maintaining and optimizing mainframe Db2 databases and data pipelines. Tasks include troubleshooting DB2 issues, backup/recovery, DDL deployments, environment refreshes, performance tuning, control reviews, and collaborating with application teams. Uses enterprise-authorized AI to accelerate design and operational tasks while ensuring validation, security, and auditability.

Belvedere Trading seeks a Senior Data Engineer to enhance trading performance through data availability and reliability, designing ETL pipelines and data architectures in a fast-paced environment.

Design, develop, test, implement, and support cloud-based data solutions. Lead and mentor engineering teams, build distributed and streaming data pipelines using big data technologies, collaborate with product managers, and ensure code quality and performance.

Lead Data Engineer responsible for designing, developing, testing, implementing, and supporting cloud-based data solutions. Collaborate across Agile teams, work with distributed systems, big data and streaming tools, data warehousing, and mentor engineers. Ensure code quality via unit tests and performance tuning, and partner with product managers to deliver scalable data experiences.

Lead design, develop, test, implement, and support cloud-based data solutions and pipelines. Collaborate across Agile teams, mentor engineers, use big data and distributed tools (Spark, Kafka, Hadoop), RDBMS/NoSQL, and cloud data warehouses (Redshift, Snowflake) to deliver performant systems that enable ML and product experiences.

The Data Engineer designs, builds, and optimizes enterprise data pipelines, lakes, warehouses, integrations, and analytics infrastructure. Responsibilities include ELT development using Snowflake and Informatica, data governance, quality, security, performance monitoring, PII protection, cost optimization, and support for analysts and data scientists. The role also delivers process improvements, analytics tools, stakeholder collaboration, technical mentoring, and scalable data architecture across cross-functional teams.

Design and implement data pipelines, ensuring data movement, transformation, storage, availability, and scalability for AI/ML and mission applications. Collaborate with software engineers and data scientists using Databricks, AWS, ETL processes, relational databases, and Agile Scrum. Build solutions supporting defense and intelligence customers, including data-driven dashboards and deployments across JWICS, NIPR, and SIPR environments.