Top Hybrid Data & Analytics Jobs

Lead and govern enterprise data architecture, including conceptual/logical/physical models, data integration, and optimization of databases and warehouses. Establish standards for data modeling, metadata, governance, quality, security, and compliance. Advise stakeholders, evaluate emerging technologies, document architecture, and mentor staff while supporting strategic initiatives.

Perform advanced workforce and talent analyses, build self-service BI products and dashboards, generate and present insights to Talent and Executive leadership, collaborate with Finance and Human Capital for workforce planning, and partner with teams to identify opportunities for deeper analytics and process automation.

Research and profile cryptoasset service providers (VASPs, custodians, exchanges) using on-chain and off-chain data. Apply AML concepts to assess risk, document findings to power entity screening and risk detection, collaborate with the Intelligence team, and help design/maintain automated pipelines and coverage processes.

Own data quality assurance and onboarding for institutional clients, standardize and validate energy, water, and carbon data, build building-level benchmarks, track jurisdictional BEPS requirements, support sustainability reporting (GRESB, TCFD, CDP), and coordinate with product, engineering, and customer teams to deliver accurate analytics and compliance guidance.

Lead design, build, and operate a secure, scalable data platform to support analytics, MLOps, and BI. Architect data warehousing (Databricks, dbt, Spark), build ETL pipelines, optimize cloud storage and processing in GCP/AWS, implement LLM infrastructure and serving, create observability and BI dashboards, and ensure data governance and regulatory compliance (HIPAA/SOC-2). Collaborate with Data Science, ML, App, and DevOps teams.

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.

Design and implement a managed AWS data reference architecture, define layered (medallion) data models and governance (PII, access, lineage, residency), create reusable engineering patterns for ingestion, storage, transformation and serving, enable loose coupling from operational systems, set governance guardrails, and occasionally build data products hands-on.

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.

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.