Top Data & Analytics Jobs

Performs business analysis and quality assurance for Murex data management initiatives in investment banking. Responsibilities include clarifying requirements and field definitions, validating data integrity and sources, running SQL analyses, documenting process flows and specifications, supporting testing and UAT, and assisting with data migration to Snowflake. The role collaborates with business and technology stakeholders to define controls, remediate issues, and support system implementation.

Build and support a Databricks-based cloud data platform for cashflow aggregation at a global bank. Responsibilities include collecting real-time and batch data, cleaning and enriching datasets, storing aggregated results in Delta Lake, supporting migration and integration, managing costs and use-case prioritization, and collaborating with data providers and consumers. The role also requires financial-services domain knowledge, communication, and leadership during the bank’s transition to Databricks.

Designs and optimizes batch and streaming data pipelines, cloud data warehouses, dimensional data models, and ETL/ELT processes at terabyte scale. Troubleshoots pipeline issues, improves infrastructure reliability and performance, implements data quality and governance practices, and supports privacy compliance. Collaborates with analysts, data scientists, and stakeholders to translate business needs into technical solutions. Leads and mentors data engineers, documents systems, and evaluates emerging technologies.

Owns and enhances a SQL Server-based data warehouse, ETL, and reporting platform supporting screening operations, billing, compliance, and customer reports. Designs SSIS packages and SSRS reports, develops optimized T-SQL, models relational schemas, resolves production issues, tunes performance, manages environment changes, and maintains auditability. Partners with product, QA, and business teams, reviews code in Azure DevOps, mentors junior engineers, and uses AI-assisted development tools.

Supports data management, integration, quality improvement, analytics, reporting, dashboard development, and automation initiatives. Responsibilities include consolidating and validating data, writing SQL queries, conducting exploratory analysis with Python or R, developing Power BI or Tableau dashboards, streamlining data pipelines, automating recurring tasks, and documenting processes.

Own and evolve Stream’s end-to-end revenue operations data platform. Build Python and dltHub ingestion pipelines, SQLMesh transformation models, dimensional data models, quality and observability systems, and GCP infrastructure centered on BigQuery. Integrate GTM systems, enable trusted analytics and reverse ETL, improve reliability and cost efficiency, and establish engineering standards. The role requires hands-on platform ownership in a fast-moving hybrid Boulder startup environment.

Design and scale cloud-based data pipelines and AI-ready platforms supporting manufacturing insights, enterprise reporting, IoT integration, and advanced analytics. Build medallion architecture data layers, integrate ERP and manufacturing data, migrate legacy systems, ensure governance and quality, and prepare datasets for machine learning use cases such as predictive maintenance and demand forecasting. Collaborate with data scientists, analytics teams, and business stakeholders to deliver scalable, trusted data products.

Leads the architecture, implementation, and evolution of enterprise data platforms, including Master Data Management, data lakes, lakehouses, and cloud data services. Manages data engineers and developers, establishes engineering standards, oversees Azure-based integrations and pipelines, and owns data applications, databases, security, governance, and production support. The role also directs technical strategy, manages multiple initiatives, and communicates architecture and delivery plans to stakeholders.

Supports the BIS team by building and maintaining Power BI dashboards, reports, data models, and DAX measures. Cleans and analyzes data from SQL Server, Excel, SharePoint, and enterprise systems; contributes to ETL pipelines, automation, validation, reconciliation, documentation, and data governance. Collaborates with business units and IT to translate requirements into actionable insights across manufacturing, supply chain, finance, quality, and operations.

Supports North American operations by developing analytical tools, writing SQL and Access reports, testing support tools, troubleshooting software and hardware issues, deploying software, and improving business processes. The role gathers business requirements, evaluates outputs, implements technology solutions, supports continuous improvement, and collaborates with users and business units. Periodic travel of up to 20% is required.

The Information Scientist will manage and extract value from global information sources, automate data workflows, apply analytical and GenAI methods, and support operational mission decision-making. The role requires library science expertise, data and information management experience, agile delivery, and the ability to develop pilots, prototypes, and minimum viable products. Desired experience includes working with dirty data, R&D environments, and high operational tempos.