Top Hybrid Data & Analytics Jobs

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.

Leads CAF’s technology, data, security, and IT strategy while managing infrastructure, cloud tools, databases, CRM, analytics, data governance, risk mitigation, telecommunications, and IT budgeting. Owns the technology product roadmap, establishes policies and standards, manages consultants, and supervises the Database Manager in support of abortion access and reproductive justice.

Leads Covista’s enterprise data engineering strategy and operations, including the cloud data lakehouse, ingestion pipelines, governance catalog, DataOps, data contracts, lineage, and PII controls. Manages platform reliability, cost optimization, technical debt, resource capacity, vendors, and proof-of-concept initiatives. Partners with data science, reporting, AI, and business stakeholders to ensure trustworthy data. Builds and mentors a diverse technical team while driving platform modernization, operational excellence, and scalable delivery.

Analyze strategic business problems using SQL, Python, statistics, data science, and AI tools. Develop KPIs, self-service analytics, automated monitoring, dashboards, and AI-powered workflows. Partner with Product, Operations, Marketing, Finance, and Clinical Operations to identify opportunities, generate actionable insights, improve forecasting, and support strategic decisions. Communicate recommendations to leadership and build scalable, data-driven operational processes.

Designs, builds, and operates scalable data platforms and ELT pipelines across Google Cloud using dbt, Python, Airflow, and BigQuery. Responsibilities include data modeling, governance, security, cost optimization, observability, infrastructure as code, incident response, and end-to-end feature delivery. The role pilots AI-assisted engineering practices, develops reusable tooling, collaborates with business and technical stakeholders, and mentors data engineers.

Design, implement, and maintain cloud-based data pipelines; integrate disparate data sources; optimize data lakes/warehouses and Spark workflows; automate Quote-to-Cash/Quote-to-Fulfillment processes; collaborate with platform, analytics, and business stakeholders to deliver scalable, reliable data solutions while ensuring data quality and security.

Own end-to-end data and analytics work for SaaS customers, including requirements gathering, data infrastructure setup, migrations, dbt reporting pipelines, SQL development, dashboards, data quality improvements, and business analysis. Collaborate directly with client data and operations teams, meet tight deadlines, and develop expertise in mortgage servicing processes.

Investigate and reconcile portfolio, benchmark, model, and custodian performance data. Analyze returns, attribution, benchmark logic, and allocation drift to identify discrepancies and root causes. Build Python diagnostics and SQL queries, maintain data-quality dashboards and audit trails, document findings, and collaborate with quant, engineering, portfolio management, and operations teams to implement validation rules and improve performance reporting reliability.