Top Data & Analytics Jobs

Owns data architecture and pipeline infrastructure for classified government AI model evaluation. Responsibilities include building database schemas, ETL workflows, API-based cross-domain data movement, sensor data standardization, geospatial transformations, pipeline monitoring, governance compliance, and documentation. The role requires collaboration with imagery scientists and government customers in a SCIF-based environment.

Analyze pricing, campaign, and portfolio performance to inform Ad Sales and Capital business decisions. Build dashboards, reports, and analytical tools; identify trends and optimization opportunities; automate recurring processes; monitor campaign and portfolio results; and translate complex data into actionable recommendations. Collaborate with Creator, Ad Sales, Finance, Engineering, and Analytics teams to develop scalable, data-driven solutions.

Own daily data operations, including data collection, pipeline monitoring, quality validation, root-cause investigation, SQL-based cleaning, and Metabase dashboards. Partner with engineering and deployment teams to resolve data issues, standardize metrics, document processes, and build tooling that improves reliability and scalability for government clients.

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 maintains scalable batch and streaming data pipelines, data warehouses, cloud data platforms, validation frameworks, and governance processes. Supports analytics through data modeling and Tableau enablement, resolves complex data issues, delivers architecture prototypes, collaborates with business and audit stakeholders, owns projects end to end, and mentors engineers. Requires advanced SQL and Python, AWS, Snowflake or Databricks, orchestration, transformation tooling, regulatory compliance, and extensive data engineering experience.

Leads consulting engagements involving scalable data architecture, real-time and batch pipelines, ETL/ELT, data modeling, cloud data platforms, governance, and production support. Designs data lakes and warehouses, develops efficient SQL and Python solutions, establishes data quality and performance KPIs, and collaborates with technical and business stakeholders. The role also includes code reviews, metadata management, and advising clients on data lifecycle and analytics capabilities.

Leads workforce intelligence and productivity analytics for Arctic Wolf’s Security Services organization. Builds productivity, capacity, cost-to-serve, savings realization, and ROI frameworks; identifies operational inefficiencies; and provides executive reporting to guide staffing, automation, product priorities, and organizational scaling. Partners with Finance, Product, Data Science, and Engineering to measure business outcomes and validate productivity improvements. Requires advanced analytics, AI/ML modeling, SQL, BI tools, and experience in 24x7 operational environments.

Business Intelligence Interns will work with stakeholders to identify analytics needs, analyze operational problems, build data models, prepare and reconcile reports, and audit logistics data for accuracy. The role also involves presenting information, applying project management techniques, meeting deadlines, and supporting additional assignments. Candidates should be college students with strong attention to detail, Microsoft Office proficiency, creativity in problem-solving, communication skills, and a willingness to learn.

Performs metadata cleanup, remediation, validation, and quality assurance in Ex Libris Alma for a federal research library. Reviews bibliographic and research metadata, verifies scholarly identifiers, applies MARC, RDA, Dublin Core, authority-control, and controlled-vocabulary standards, and uses Python or related tools for analysis and automation. Executes approved changes within governance and change-control procedures, maintains audit documentation, prepares progress reports, and collaborates with library, technical, and government stakeholders.

Build and optimize scalable data pipeline architecture, assemble complex datasets, improve data delivery infrastructure, and support data initiatives across engineering, product, BI, and data science teams. The role requires production-scale software engineering experience focused on data and ML processes, expertise with Spark, Kafka, SQL, and a programming language such as Python, Scala, or Java, plus knowledge of databases, data modeling, and ML operations.