Top Data & Analytics Jobs

Work at the intersection of software engineering and operations to investigate production issues, improve reliability, build automation, enhance observability, and support customers. Develop and maintain C# and SQL Server solutions, reduce manual effort, convert production learnings into lasting improvements, and provide occasional after-hours support.

Build, maintain, and optimize cloud-based data pipelines and ETL processes. Collaborate with business stakeholders to design data models and deliver scalable warehousing solutions. Implement data quality checks, monitor performance, document systems, and enable self-service analytics (Power BI). Support migration from legacy systems and drive data engineering best practices across environments.

Lead mixed-methods research with a quantitative focus: design analytics, run experiments, analyze product usage, build dashboards, enable self-service, support product discovery, and translate findings to influence product strategy.

Lead architecture and operations for the end-to-end data lifecycle: pipeline orchestration, dbt projects, cloud warehouse performance, dimensional modeling, data observability, HIPAA controls, and platform governance. Mentor a distributed data engineering team, partner with Analytics and Finance, and prioritize durable platform capabilities that enable BI, experimentation, and AI use cases.

Build, run, and maintain end-to-end data pipelines for mass spectrometry and molecular datasets. Ingest, convert, label, and enrich messy scientific data; implement data quality checks, monitoring, tests, and reproducible builds for ML training and product-read datasets.

Design and own end-to-end data architectures for finance clients: modelling, pipelines, governance, lineage, and cloud platforms to support analytics and production AI. Lead architecture decisions, mentor engineers, set standards, and ensure secure, auditable, cost-aware data estates that enable RAG, feature stores, and reliable reporting.

The Manager of Business Analytics - Fraud leads efforts to develop and optimize fraud risk strategies, improve monitoring frameworks, and support product launches through analytical insights on fraud risks.

Design and maintain dashboards and reports, perform deep-dive analyses to identify trends and anomalies, ensure data integrity with engineers and data scientists, automate reporting, and translate complex datasets into clear insights for non-technical stakeholders.

Lead global data engineering strategy, architecture, and operations for data platforms and pipelines. Own roadmap, build scalable batch and real-time pipelines, ensure data quality, governance, security, and compliance. Grow a distributed team, partner with cross-functional leaders, manage budgets, and drive reliability and cost-effective platform performance at high transaction volume.

Design and implement enterprise data architecture and ETL/ELT on GCP (BigQuery). Own ingestion, transformation, modeling, orchestration, governance, and platform roadmap. Collaborate with business and BI teams, mentor engineers, drive modernization (lakehouse, data mesh, streaming), and ensure scalable, secure, high-performance data products for analytics.