Top Data & Analytics Jobs

Lead the architecture and delivery of end-to-end Azure data platforms, including ingestion, transformation, modeling, analytics, activation, APIs, and agentic AI integrations. Build production-grade batch and streaming pipelines using Microsoft Fabric, Azure Databricks, PySpark, Python, and SQL. Establish governance, security, lineage, and performance standards; troubleshoot critical systems; mentor engineers; advise client stakeholders; contribute to presales and reusable engineering frameworks.

Analyze and maintain manufacturing data systems, develop KPI dashboards, ensure reporting accuracy, and provide data-driven insights for engineering projects and cause investigations. Collaborate with operations, process engineering, quality, and manufacturing associates, occasionally collecting data on the shop floor and supporting compliance with quality standards.

Build and maintain Azure-based ETL and ELT pipelines supporting a layered cloud data lakehouse. Integrate ERP and operational data through REST APIs and SQL sources, implement incremental loading and schema evolution, manage orchestration workflows, and monitor data quality and pipeline health. The role also includes data modeling, documentation, testing, deployment practices, code reviews, and collaboration with leadership while developing toward broader engineering ownership.

Analyze, cleanse, and manage investment data from multiple sources, evaluating vendor data quality and supporting portfolio management, trading, compliance, and reporting systems. The role develops business logic, data structures, pipelines, testing processes, and automation while helping define new data products and improve operational efficiency. It also requires collaboration with internal stakeholders and data engineers, plus mentoring junior analysts.

Design and build a durable data storage platform that preserves source disagreements, provenance, uncertainty, historical versions, and evidence. Develop result and evidence stores, lineage tracking, versioned rules and configurations, reproducible decision records, and point-in-time entity views. Collaborate with entity-resolution and machine-learning teams to support probabilistic outputs. The role requires production data engineering experience, strong Python and SQL, PostgreSQL expertise, U.S. citizenship, and an active Secret clearance.

Bachelor-level intern supporting behavioral health operations through client interaction, public speaking, team meeting facilitation, and data work. The intern will receive training in Microsoft Excel, data cleaning and analysis, data visualization, and storytelling through one-page reports and presentations. The role requires strong written and verbal communication, attention to detail, independent work, and the ability to manage multiple priorities.

Review robot task videos and annotations for accuracy, provide feedback to annotation teams, refine quality guidelines, coach teleoperation pilots, monitor data and performance metrics, and improve operational processes. The role partners with Autonomy and Operations teams to support reliable, high-quality datasets for training and evaluating robotic systems.

Designs and maintains scalable, low-latency data pipelines for real-time and batch market data. Builds ETL/ELT processes, optimizes storage and streaming systems, supports productionizing AI and machine-learning models, and improves data quality, observability, reliability, and cost efficiency. Collaborates with data scientists, ML engineers, and product teams to evolve Fetcherr’s data platform.

Reviews project requirements, analyzes and maps vendor files to CU*BASE data, validates files, researches issues, and coordinates projects with vendors, credit unions, and internal teams. The role tracks timelines, communicates changes, follows programming standards, handles sensitive data, and supports multiple projects while maintaining accuracy and deadlines.

Conducts data collection, cleaning, management, statistical and computational analysis for education policy research. Responsibilities include building reproducible ETL pipelines, creating visualizations and research materials, applying machine learning and natural language processing, validating model outputs, managing restricted-use data, reviewing analyses, and coordinating with external collaborators. Senior responsibilities include overseeing analytical projects, documenting methodologies, managing compute and API costs, and serving as a technical contact.