Top Data & Analytics Jobs

Designs, builds, and maintains scalable AWS and Snowflake data pipelines, ETL/ELT processes, and dbt transformation models. Ensures data quality, reliability, security, governance, privacy, and performance through testing, profiling, validation, and optimization. Partners with engineering, analytics, product, and business stakeholders to deliver maintainable data solutions, participates in planning and code reviews, documents work, and improves platform automation.

Develops data-processing workflows, pipelines, and analytics for desktop and web-based visual analytics tools. Performs large-scale relational data analysis using visualization and high-performance computing, improves system performance and secure data practices, monitors junior team members, and contributes to cross-team technical standards.

Designs, builds, deploys, and maintains scalable cloud-native data platforms, pipelines, data lakes, warehouses, and analytics solutions. Supports cloud migration, data science, AI/ML, and business intelligence initiatives while processing structured and unstructured data. Collaborates with analysts, developers, engineers, and data consumers in agile teams, provides technical guidance, and evaluates cloud analytics technologies and architectures.

Build, test, document, and optimize data pipelines and foundational datasets for Figma’s data warehouse. Interns will support data quality, scalability, analytics, experimentation, and cross-functional data needs while owning a scoped project from definition through implementation, testing, documentation, and results sharing.

Owns the Digital team’s data foundation, BI ecosystem, and performance measurement framework. Designs data pipelines, ETL/ELT processes, governed datasets, semantic models, dashboards, KPI frameworks, and executive reporting. Develops financial models, supports experimentation and AI-enabled analytics, and translates customer, operational, and financial data into actionable growth recommendations. Serves as the subject matter expert for data governance, metric definitions, reporting quality, and analytics best practices while partnering with senior leaders and cross-functional teams.

Builds and architects complex ETL pipelines and data integrations for an enterprise data lakehouse. Responsibilities include designing workflows, monitoring data movement across cloud and on-premise systems, improving data quality, implementing master data management, documenting lineage, troubleshooting integrations, and standardizing data processes. The role collaborates with business units, analysts, visualization engineers, IT, and data scientists to enable new data sources and resolve complex technical issues.

Lead migration to a Databricks lakehouse using dbt, design ELT pipelines, improve ingestion reliability, establish data quality and governance, and build analytics- and AI-ready datasets. The role owns platform architecture, access controls, monitoring, documentation, data contracts, and legacy-system retirement. It partners with engineering, data science, AI, analysts, and business stakeholders while mentoring engineers and influencing technical standards.

Leads and develops a data engineering team implementing Navigate360 integrations for partner campuses. Oversees integration design, data mapping, validation, reconciliation, quality controls, delivery planning, and scalable implementation processes. Partners with Product, Launch, and Partner Success teams to resolve technical and partner challenges, improve adoption, manage project health, and ensure reliable outcomes. Provides technical mentorship, code and design review, stakeholder communication, and continuous process improvement.

Designs and maintains cloud data pipelines, data platforms, ETL and real-time streaming architectures. Builds PySpark jobs, AWS Lambda workflows, EventBridge orchestration, Aurora PostgreSQL schemas, Kafka producers and consumers, and Snowflake solutions. Responsibilities include data governance, lifecycle management, replication, query optimization, reporting, anomaly analysis, standards development, and mentoring team members. The role modernizes legacy systems and supports enterprise analytics initiatives.

Build and maintain data pipelines using ETL/ELT, Databricks, Python, Spark, and Airflow. Manage data transformations, databases, schemas, and data quality controls. Support reporting, analytics, and machine learning integrations while deploying solutions in AWS. Automate workflows, troubleshoot data issues, document processes, and collaborate with data scientists, analysts, database administrators, and business stakeholders.

Own Qualitate’s data engineering foundation by building pipelines for structured and unstructured data, designing relational and non-relational data models, and developing AI-powered enrichment workflows. Establish data quality monitoring, integrations, schemas, contracts, lineage, and documentation. Partner with Applied AI, Research, and Engineering while shaping the company’s early data architecture and enabling customer data integrations.

Build and optimize scalable data architectures, pipelines, warehouse tables, and data feeds from diverse sources. Ensure data quality, monitoring, CI/CD reliability, security, and efficient compute and storage usage. Collaborate with engineering, product, and cross-functional teams on data analysis, AI, machine learning, experimentation, and ad technology products. Develop automated data integration and cleansing processes, troubleshoot pipeline failures, and contribute to complex projects involving cloud platforms, MLOps, and large datasets.

Develop and maintain scalable data pipelines, streaming systems, data warehouses, and analytics infrastructure using Kafka, Flink, ClickHouse, SQL, Java, Python, AWS, and related technologies. Assemble complex datasets, improve data delivery and process efficiency, validate data integrity, support Tableau analytics, document business processes, assist with testing and project delivery, and collaborate with technical and business stakeholders.

Serves as a data-management subject matter expert supporting intelligence and counterintelligence organizations. Establishes data frameworks, governance policies, metadata standards, quality controls, interoperability processes, KPIs, dashboards, and analytical products. Advises leadership on modernization, resource allocation, and operational planning; coordinates with data scientists, engineers, security personnel, and Intelligence Community stakeholders. Requires active TS/SCI CI Poly and on-site work in a DIA SCIF.

Design, build, and own business-critical data pipelines and infrastructure across warehouses, orchestration, BI, and multiple regions. Develop AWS Glue and Spark jobs, data models, event contracts, observability, and batch or streaming architectures. Collaborate across producer and consumer teams, support Federal workloads, and operate production data platforms in government cloud environments.

Develops and maintains Tableau dashboards supporting mission, programmatic, and technical decision-making. Responsibilities include validating data sources, modeling and transforming data, integrating enterprise platforms, gathering stakeholder requirements, testing visualizations, documenting data pipelines, and advising on dashboard architecture and performance. The role supports Government and Intelligence Community stakeholders in a SETA environment and requires executive-level communication, systems engineering knowledge, and an active TS/SCI clearance with polygraph.

Design, build, and scale data pipelines, ETL workflows, analytics infrastructure, and machine-learning data systems. Ensure data quality, security, monitoring, and reliability while optimizing storage and processing performance. Collaborate with engineering, product, and analytics teams on data requirements, modeling, schemas, and scalable infrastructure. The role also establishes data engineering best practices and supports a data-driven culture.

Designs and evaluates data architectures, ETL pipelines, storage strategies, and governance practices supporting Marine Corps AI capabilities. Advises leadership and vendors on data maturity, security, rights, interoperability, and deployment across cloud, on-premises, and tactical edge environments. Establishes data quality standards, analyzes data flows, supports experiments and wargames, and documents technical assessments. Requires substantial data engineering experience, U.S. citizenship, and TS/SCI eligibility.

Develop, operate, and maintain distributed parallel streaming and batch data-processing pipelines in classified environments. Responsibilities include configuring data connections, parsing, normalizing, mapping, modeling, enriching, correlating, and deduplicating data; integrating data and analytics SDKs/APIs; applying Agile SDLC practices; maintaining CI/CD pipelines; and updating technical documentation, procedures, and training materials.

Design, build, and lead scalable data platforms, pipelines, data products, and ELT/ETL processes supporting analytics and AI/ML workloads. Establish standards for data quality, testing, monitoring, observability, CI/CD, security, and reliability. Own projects through deployment and production support, collaborate across technical and business teams, use AI-assisted engineering tools, and mentor data engineers.

Develop and support scalable data products, cloud data lake platforms, enterprise data pipelines, and analytical data services. Responsibilities include coding, data transformation, debugging, root cause analysis, testing, infrastructure as code, DevOps automation, data governance, quality improvement, and collaboration with technical and business teams.

Build and scale data insights capabilities, write SQL queries in Snowflake, transform data with Python, and create dashboards in Qlik Sense and Tableau. Support data governance, preprocessing pipelines, predictive modeling, documentation, version control, and automated data workflows. Collaborate with Data Engineering teams to deliver real-time insights for robotics and supply chain automation projects.

Lead the design and development of Azure-based big data solutions, including batch and real-time ETL/ELT pipelines, data architecture, ingestion, curation, warehousing, and storage. Build solutions using Azure Data Factory, Databricks, Synapse, Spark, Python, SQL, and related Azure services. Support Salesforce migration and integration while applying strong RDBMS, data management, and big data design expertise.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

Owns loyalty, customer, and transaction data platforms end to end, including ingestion, architecture, pipelines, modeling, governance, integrations, monitoring, and reporting. Partners with business leaders and vendors to solve ambiguous data problems, improve reliability, and modernize scalable solutions. Integrates AI, LLMs, and agentic frameworks into analytics workflows while ensuring data quality, security, and cost effectiveness.