Top Data & Analytics Jobs

Build scalable cloud data infrastructure and pipelines to transform transactional and financial data into reporting-ready datasets. Ensure accuracy, traceability, governance, and observability; partner with cross-functional teams, mentor engineers, and leverage AI-assisted tools to improve engineering productivity and enable AI-powered applications.

As a Lead Data Engineer, you will design and implement data solutions using big data technologies, collaborate with Agile teams, and mentor other engineers.

As a Senior Lead Data Engineer, you will design, develop, and support data solutions, optimize systems, and collaborate within Agile teams to leverage technology for business transformations.

The Lead Data Engineer will design, develop, and support complex technical solutions using Java, Python, AWS, and big data technologies. Responsibilities include collaborating with Agile teams, mentoring, and delivering cloud-based solutions.

The job involves leading a team to develop data-driven solutions, utilizing various programming languages and cloud technologies, while collaborating with Agile teams and mentoring junior members.

Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

Design, build, and maintain Python-based data applications and scalable data pipelines using AWS Glue and PySpark; deploy containerized services on EKS; implement APIs with API Gateway and Lambda; integrate PostgreSQL, DynamoDB, and Kafka; follow TDD, CI/CD, and document work in Confluence and JIRA while collaborating in Agile teams.

Designs, builds, and maintains an ontology-grounded enterprise semantic map and metadata graph. Develops automated metadata ingestion, ETL pipelines, API integrations, graph database schemas, lineage and provenance models, and graph queries. Aligns data assets with enterprise ontologies, governance requirements, and mission metadata. Enables semantic search and AI agents, optimizes graph performance, documents architecture, and mentors engineers. Requires production data engineering, graph database, systems integration, and graph query experience, plus an active TS/SCI clearance.

Supports development and maintenance of an ontology-grounded enterprise metadata graph. Responsibilities include automating data-source discovery, ingesting technical metadata, integrating APIs and catalogs, mapping assets to ontologies and provenance layers, maintaining graph databases, supporting lineage and governance metadata, writing basic graph queries, and enabling semantic search and AI-agent workflows. Collaborates with senior engineers and AI teams to connect data assets to enterprise mission workflows.

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.

Lead the architecture and deployment of scalable data platforms, real-time and batch pipelines, AI infrastructure data systems, APIs, data lakes, warehouses, streaming architectures, and data quality frameworks. Optimize distributed processing and queries, establish governance, model enterprise data semantics, evaluate modern data technologies, and mentor engineering teams while collaborating across analytics, product, and platform groups.

Design, implement, and maintain secure, scalable enterprise database and geospatial data platforms for mission-critical Defense and Intelligence systems. Responsibilities include database architecture, configuration, backup and recovery, high availability, disaster recovery, performance tuning, security controls, automation, data loading, migrations, integrations, and technical guidance. The consultant collaborates with customers and cross-functional teams throughout implementation and communicates solutions to technical and non-technical stakeholders.

Design and implement secure, scalable geospatial database platforms for defense and intelligence operations. Responsibilities include database administration, performance tuning, backup and recovery, high availability, disaster recovery, access controls, compliance, automation, data integration, and technical guidance. The role also involves collaborating with customers and cross-functional teams throughout requirements, design, implementation, and deployment.

Designs, implements, secures, and optimizes scalable enterprise database and geospatial data platforms for mission-critical Defense and Intelligence systems. Responsibilities include database architecture, configuration, backup and recovery, high availability, disaster recovery, performance tuning, access controls, compliance, automation, data loading, migrations, integrations, and customer-facing technical guidance.

Manage, design, and optimize data activities for federal IAM systems. Develop data models, file layouts, source-to-target mappings, data dictionaries, integrations, ETL processes, and analytics support for identity analysis and fraud detection. Administer and tune databases, including configuration, backups, monitoring, capacity planning, and performance optimization. Support secure handling of identity and PII data, network-related data flows, documentation, token management processes, and collaboration across development, security, and operations teams.

Build and maintain ELT pipelines, data models, semantic layers, and personalization signals for client retention and marketing tests. Partner with machine learning, product, marketing, design, and architecture teams to prepare predictive data, connect signals to CRM channels, support real-time event processing, and document a scalable data foundation. Use AI coding assistants daily to accelerate development, testing, and reviews in a fast-moving discovery environment.

As a Data Engineer, you will create efficient data models, pipelines, and analytics frameworks, collaborating with various departments to transform raw data into actionable insights.

Design, build, and maintain scalable AWS-based data pipelines and storage (S3, Redshift, RDS, DynamoDB). Implement IaC (CloudFormation/CDK), ETL optimization, monitoring, security (IAM, OWASP/CIS best practices), and CI/CD/code pipeline controls. Collaborate with product teams, document solutions, and provide training to support analytics and reliable data infrastructure.

Build and maintain reliable ETL and real-time data pipelines, enable data-driven product decisions, ensure relevant event collection, align data roadmap to business goals, and collaborate with platform, security, and analytics teams to deliver actionable insights.

Embedded data engineer on the systematic China equity team responsible for end-to-end data lifecycle: sourcing, ingestion, modeling, validation, transformation, and delivery for research and production. Build scalable data pipelines, schemas, quality checks, and monitoring; profile datasets; collaborate with researchers, developers, and vendors; and improve tooling, standards, and vendor relationships to support alpha research and trading.

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.

Design, build, and operate scalable data pipelines and platforms for enterprise clients. Develop ETL/ELT workflows, data ingestion and processing for structured and unstructured data, guide cross-functional teams, and support monitoring, scaling, and operationalization of data solutions.

Design, build, and maintain scalable data pipelines and platforms. Implement ETL processes, collaborate with cross-functional teams, support analytics and ML workloads, and ensure data accessibility for mission-driven projects. Work occurs in agile teams and may involve cloud and on-prem infrastructure.

The Data Engineer, Senior will design and maintain data systems, build ETL pipelines, and collaborate with a team to optimize data solutions for client missions.