Top Data & Analytics Jobs

Design and manage enterprise data architecture, models, integration pipelines, governance, metadata practices, and cloud data platforms. Build data structures supporting reporting, analytics, AI, and self-service business intelligence. Provide technical oversight through architecture and code reviews, collaborate with engineering and analytics teams, and maintain documentation. The role requires extensive SQL, data modeling, cloud architecture, data pipeline, source control, DataOps, and CI/CD experience, along with strong communication and project prioritization skills.

Supports brand and digital analytics by reviewing and tagging social media content, analyzing large datasets, preparing recurring and ad hoc reports, and developing dashboards. The role collaborates with platform partners and internal stakeholders, performs quantitative data pulls, communicates insights, and uses content management, social listening, visualization, and generative AI tools to improve reporting workflows.

Partners with Corporate Sales to analyze market, audience, sponsorship, category, and consumer data. Develops commercial insights, identifies revenue opportunities, shapes partnership strategy, and translates complex findings into actionable recommendations and compelling narratives. Presents strategic insights to senior stakeholders and collaborates with commercial intelligence, valuation, brand strategy, and creative teams. Requires strong presentation, storytelling, analytical, and commercial strategy skills.

Process geospatial customer deliveries through Python and AWS pipelines, validate and publish map layers, build automated quality checks, investigate processing failures, and document fixes. Automate recurring data operations, measure efficiency gains, and partner with engineering and customer success to resolve field-reported issues. The internship requires full-time availability for 10–12 weeks in the Boston office.

Conduct quantitative investment research using financial datasets, improve investment approaches and security-selection algorithms, and analyze new opportunities. The intern will lead a research project, communicate technical findings, collaborate with quantitative investment professionals, and gain experience applying data-driven methods to global capital markets.

Analyzes contact center call volume, staffing, schedule adherence, KPIs, and performance trends. The role manages real-time staffing adjustments, overtime and time-off planning, exception scheduling, agent skill levels, and workforce data in Alvaria and NiCE CMS. It produces reports and recommendations for leadership, investigates missed KPIs, supports contact center managers, maintains workforce system records, and provides training for new hires.

Analyzes contact center call volume, staffing, schedule adherence, and performance metrics to support KPI achievement. Maintains Alvaria and NiCE CMS workforce management data, schedules, staffing levels, PTO allowances, and agent skills. Produces reports and recommendations for management, investigates performance variances, supports real-time operational adjustments, and collaborates with contact center leaders to improve efficiency and agent satisfaction.

Develop and maintain data engineering solutions using Informatica Data Management Cloud, Control-M, Snowflake, and SQL. Responsibilities include sourcing, transforming, cleansing, ingesting, analyzing, and visualizing data; building automated workflows and scalable ETL processes; supporting data architecture and deployments; and delivering reusable code for large datasets. The role requires collaboration with technical and business partners, troubleshooting, mentoring, and managing multiple priorities.

Design and build scalable data infrastructure, pipelines, APIs, and analytics systems using Snowflake, AWS, Airflow, and DBT. Optimize performance, reliability, data quality, and cloud costs while enabling real-time and batch analytics. Partner with cross-functional teams on reporting, governance, and compliance initiatives. Mentor engineers, lead architecture decisions, conduct design reviews, and contribute to technical standards and hiring.

Lead enterprise data architecture strategy and modernization across Microsoft Fabric, Azure, OneLake, lakehouses, warehouses, and integration platforms. Establish standards for modeling, ETL/ELT, governance, security, metadata, lineage, and data quality. Define AI-ready architecture patterns, lead architecture reviews, mentor engineers, and create reusable reference architectures. Drive migration from legacy platforms to Microsoft Fabric while promoting specification-driven development and scalable analytics, reporting, operational intelligence, and AI capabilities.