Top Hybrid Data & Analytics Jobs

Build and maintain data pipelines in Azure Synapse using Spark, Python, SQL, and C#. Apply data warehouse and lakehouse modeling standards, collaborate with analysts and testing teams, troubleshoot data issues, and develop machine learning and regression analysis capabilities using Azure ML and related tools.

Analyze marketing and digital marketing performance across social, web, app, and email channels. Develop segmentation, attribution, funnel, conversion, retention, KPI, dashboard, and competitive analyses. Design A/B and multivariate experiments, identify data issues, and translate statistical findings into actionable recommendations for stakeholders and senior leaders. Collaborate with technical teams to improve data reliability and support consumer acquisition, retention, and product performance.

Own contact center KPI analysis covering volume, handle time, adherence, occupancy, and service level. Build Tableau or Power BI dashboards, identify trends and root causes, and provide actionable recommendations to operational leaders. Manage multiple analytical projects, prioritize changing requests, influence stakeholders without direct authority, and communicate findings through data storytelling. Use AI tools responsibly to accelerate analysis while maintaining data quality, compliance, and human judgment.

Manages supplier master data governance, including onboarding, maintenance, validation, cleansing, audits, reporting, and data quality improvement. Identifies and resolves inaccurate, duplicate, or incomplete records; supports ERP enhancements and governance initiatives; performs supplier risk and fraud controls; and collaborates with stakeholders and suppliers on compliance, process improvements, and user education.

Own Orum’s data platform architecture, roadmap, reliability, and scalability. Build batch and real-time ETL/ELT pipelines using PostgreSQL, event streams, ClickHouse, BigQuery, and dbt. Administer Looker and develop LookML models, dashboards, and a trusted semantic layer. Establish data quality, governance, lineage, observability, and metric standards while partnering with Product, Finance, GTM, and Operations on analytics, AI, and business intelligence initiatives.

Analyze structured and unstructured data from multiple sources to identify trends, produce dashboards and reports, and deliver actionable business insights. Develop BI solutions, KPIs, forecasts, root-cause analyses, and recommendations for improving operational performance. Communicate findings to technical and senior stakeholders, fulfill ad hoc reporting needs, and lead small projects and initiatives.

Designs and scales cloud-native data platforms and ingestion pipelines using AWS, Databricks, and Spark. Builds APIs, file-upload mechanisms, ETL/ELT workflows, data transformations, and Lakehouse architecture supporting analytics, machine learning, and operational use cases. Provides operational support for legacy C# .NET ingestion processes, troubleshoots pipelines, and promotes data governance, security, reliability, and documentation in a multi-tenant SaaS environment.

Administer, design, optimize, monitor, and support DB2 for z/OS, SQL Server, Oracle, and GIS databases. Responsibilities include schema and data architecture development, performance tuning, SQL code reviews, recovery and disaster-recovery support, administration procedures and documentation, infrastructure planning, change verification testing, and mentoring technical staff. The role requires extensive mainframe expertise, including JCL, COBOL, RACF, CICS, monitoring tools, and z/OS platform administration.

Design, build, and operate complex Apache Airflow workflows, Snowflake and dbt data transformation pipelines, and distributed data science inference systems across multi-cloud environments including AWS and GCP. Collaborate with an experienced data engineering team while applying systems engineering and product lifecycle principles.

Develop software modules and scalable data infrastructure for audio machine learning research. Generate, augment, and simulate audio data while supporting data platforms and pipelines. Collaborate with research scientists, data scientists, and software engineers on prototyping and innovative audio ML use cases. The hybrid internship is based in Niles, Illinois, and runs for twelve weeks during summer 2027.