Top Data & Analytics Jobs

Leads the strategy, architecture, development, and operation of scalable data and AI platforms. Oversees data pipelines, Snowflake architecture, AWS infrastructure, data quality, observability, governance, and analytics enablement. Guides machine learning and generative AI data foundations, including embeddings, vector search, RAG, model pipelines, and evaluation. Mentors engineers, establishes technical standards, manages cross-functional initiatives, and partners with executives and business stakeholders on AI and data priorities.

Leads data science and knowledge management efforts by tracking research questions, governing metadata, improving data discoverability, developing tagging and classification schemes, and integrating research products into dashboards. Uses Databricks Unity Catalog, OpenLineage, and ESRI metadata APIs while applying Department of War and Intelligence Community tradecraft standards. Requires TS/SCI clearance and supports classified client environments.

Researches and develops equity execution models, transaction cost models, order-routing strategies, and analytical tools. Applies statistical, mathematical, computational, and machine learning techniques to forecasting, sentiment analysis, execution optimization, performance measurement, and risk attribution. Collaborates with electronic trading, development, technology, and model validation teams to assess, document, maintain, and deploy commercially viable trading algorithms.

The Staff Data Reliability Engineer ensures data integrity, quality, freshness, and availability across enterprise pipelines. Responsibilities include defining data SLOs, implementing observability, building resilient Informatica and Python/PySpark pipelines, automating validation and recovery, eliminating operational toil, leading data incidents and postmortems, creating runbooks, and applying AIOps, machine learning, and DataOps practices to detect anomalies and self-heal data workflows.

Develop and maintain SQL-based data views, ETL workflows, and cloud data warehouse processes supporting CMS healthcare research data. Ingest new data sources, optimize database performance, document business logic, support production data loads, and collaborate with analysts, technical leads, and CMS partners. The role also involves data quality, monitoring, permissions, recovery, troubleshooting, and occasional off-hours or weekend production support.

Analyzes operational performance and financial-impact data for Philadelphia’s Water Department. Responsibilities include acquiring and cleaning large datasets, building data models and views, applying statistical and machine learning methods, identifying patterns, automating predictive and prescriptive analytics, and communicating insights to stakeholders. The role supports resource allocation, asset management, leak detection, water-loss mitigation, and revenue integrity. It requires onsite work, Philadelphia residency, off-hours availability, and participation in an on-call rotation.

Supports the enterprise data and analytics platform by building Microsoft Fabric lakehouses, warehouses, pipelines, semantic models, and Power BI reporting. Integrates D365, manufacturing, warehouse, supply-chain, IoT, and operational data; develops SQL and DAX solutions; implements data quality, governance, security, monitoring, and documentation; and troubleshoots production issues. The role also supports AI-enabled analytics and collaborates with business teams, vendors, and technical stakeholders in a manufacturing environment.

Manage data analysis for transaction monitoring, risk identification, and compliance. Analyze high-volume transaction data using SQL, Python, Splunk, and visualization tools; conduct root-cause investigations; design risk data architectures; identify process and reporting improvements; advise business and product teams; and translate technical findings into executive recommendations. The role also involves stakeholder liaison, regulatory alignment, data integrity, and managing concurrent analytical projects.

Supports data extraction, cleaning, analysis, visualization, ETL pipeline development, automated scripting, and machine learning model evaluation. The intern collaborates with data scientists and cross-functional teams, develops analytical tools, delivers actionable insights, performs ad hoc analyses, and presents project accomplishments to leadership. The three-month spring internship is intended for a currently enrolled bachelor’s student fulfilling a graduation requirement.

Designs, develops, and maintains scalable data warehouses, data lakes, platforms, and ETL/ELT pipelines. Builds batch and streaming workflows, processes structured and unstructured data, optimizes cloud storage and pipeline performance, and implements data governance and lifecycle management. Guides multidisciplinary teams in agile environments while delivering mission-focused data engineering solutions.

Leads analytics strategy for Faire’s Discovery pillar, informing search, recommendations, ads, and marketplace decisions. Builds and manages a high-performing strategy and analytics team, establishes analytical rigor and experimentation standards, owns key metrics and datasets, and develops self-serve data tooling and operating systems. Partners cross-functionally to identify opportunities, improve decision-making, and scale analytics practices across the company.

Manage and support analytics initiatives, mentor analysts, prioritize business-critical projects, and develop scalable data processes. Conduct data modeling and analysis, maintain data quality, automate workflows, and translate complex findings into actionable recommendations. Partner with product managers, business leaders, clients, and executives to advance data-driven decision-making while supporting compliance, reporting standards, and stakeholder needs.

Supports data engineering, analytics, data governance, dashboard development, ETL, and reporting projects across business and IT teams. Interns may build Power BI dashboards, analyze data trends, improve data quality, develop analytics solutions using AI and machine learning tools, and document complex processes. The role requires collaboration with stakeholders, strong communication, and familiarity with Python, SQL, cloud platforms, ETL frameworks, and reporting technologies.

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.

Design and build resilient data pipelines integrating files, APIs, services, databases, and mission sources. Develop extraction, normalization, enrichment, validation, cataloging, routing, ingestion, and event-driven workflows. Preserve geospatial metadata, provenance, lineage, security attributes, and data quality. Apply AI, RAG, MCP, and agentic capabilities to improve data discovery and access. Build Node.js applications and Oracle Database integrations while collaborating with engineering, cloud, security, AI, and mission teams.

Supports data engineering, analytics, and research initiatives for Mercury Marine. Responsibilities include building data processing pipelines in Microsoft Fabric and Synapse, analyzing telemetric datasets, responding to ad hoc data requests, applying statistical methods, and presenting findings to technical and non-technical stakeholders. The role is designed for students nearing completion of a data science, computer science, statistics, or related degree.

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.

Data Engineering Interns support analytics, data intelligence, and IT initiatives by developing dashboards, preparing and analyzing data, supporting data governance, and helping build ETL solutions. Responsibilities may include Power BI reporting, KPI analysis, identifying data trends, collaborating with stakeholders, and exploring AI and cloud technologies. Candidates gain mentorship and experience while applying Python, SQL, cloud platforms, and analytics tools to business projects.

Build and maintain scalable batch and real-time data pipelines for financial and alternative datasets. Productionize AI/ML workflows for document extraction, classification, and data processing; own model evaluation, monitoring, and human review. Design investment data models, validation, anomaly detection, and reconciliation systems. Partner with quants and data scientists to operationalize research signals, onboard vendors and datasets, integrate APIs, and improve data reliability using modern cloud, streaming, orchestration, and warehouse technologies.

Assistant Professor of Business Analytics responsible for designing and leading a new undergraduate program, teaching business analytics courses, advising students, conducting scholarly research, serving the university community, and engaging with professional associations. The role requires expertise in data collection, databases, statistical analysis, machine learning, data visualization, enterprise resource planning, and AI in business. Applicants must support Calvin University’s Christian educational mission and demonstrate commitment to diversity and inclusive communication.

Owns enterprise data architecture, standards, semantic layers, certified data products, and data governance across large-scale transformation programs. Leads SAP S/4 data transformation, Databricks integration, migration strategy, architecture reviews, vendor assessments, and technical delivery direction. Establishes data quality, security, access-control, and product operating models while influencing executives and cross-functional teams without direct reporting authority. Mentors architects and engineers and guides multi-year platform and technology investments.

Architects and develops scalable big data pipelines, ETL processes, real-time analytics frameworks, and cloud data infrastructure for battery and time-series data. Ensures data quality, designs storage and data models, supports production AI/ML workflows, and integrates model outputs into pipelines. Translates business and engineering needs into functional specifications while coordinating internal and external stakeholders.

Leads General Liability insurance pricing analytics, predictive modeling, model governance, and data science strategy. Develops, validates, deploys, and monitors advanced statistical and actuarial models supporting underwriting, profitability, and pricing decisions. Partners with actuarial, underwriting, product, claims, finance, and technology teams; communicates recommendations to senior leaders; manages complex projects; and leads, coaches, and develops analytical staff.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.

Leads and builds Instructure’s decision science function, including hiring, team development, roadmap ownership, experimentation, causal and predictive modeling, executive insights, and data literacy. Partners with product, engineering, marketing, finance, and executives to guide product, pricing, retention, go-to-market, and operational decisions. Establishes analytical standards, supports data infrastructure, and ensures ethical, privacy-conscious use of student data.