Top Data & Analytics Jobs

Leads the data engineering team, including hiring, coaching, technical direction, platform architecture, data pipelines, Snowflake warehousing, governance, quality, observability, and incident support. Partners with Data Science, Analytics, Clinical Operations, Biostatistics, and Business Development to deliver trusted data products for clinical operations, asset evaluation, reporting, and machine learning. Oversees budgeting, resourcing, documentation, technical programs, and responsible use of AI engineering tools.

Manage and validate merchant data integrations that feed Riskified’s data science models. Responsibilities include configuring data transformations, onboarding clients, assessing data flows, creating JSON and CSV templates, writing Python, SQL, Spark, Databricks, or R scripts, wrangling large datasets, monitoring data alerts, resolving data issues, and collaborating with engineering, product, data science, research, account management, and sales teams.

Implement StrataJazz software for healthcare system customers by translating integration specifications, configuring ETL processes, and using SQL and Excel to solve data problems. Partner with customers and internal technical teams, lead training, communicate project risks, execute mitigation plans, and support operational improvement initiatives. The role involves healthcare financial and clinical data, Agile implementation practices, and up to 10% domestic travel.

Leads the design, development, and maintenance of enterprise data systems supporting manufacturing data, analytics, reporting, and AI. Oversees scalable cloud-based solutions, data pipelines, infrastructure, modeling, formats, and frameworks. Partners with stakeholders to align data capabilities with business needs and manages a high-performing data engineering team through coaching, performance management, and organizational development.

Own and maintain complex datasets, data processes, and client-facing dashboards. Partner with account, strategy, operations, and product teams to deliver data products, identify analytics opportunities, and improve business intelligence capabilities. Lead cross-functional project engagements, developing team processes and client deliverables while translating client requirements into effective data solutions.

Leads the design, development, and ownership of scalable data systems, ELT pipelines, data models, and Snowflake architectures supporting analytics, experimentation, machine learning, and business decisions. Partners cross-functionally to deliver reliable, governed data solutions; improves quality, observability, testing, documentation, and operational practices; mentors engineers; and shapes technical direction through automation, technology evaluation, and AI-enabled engineering.

Leads and develops a Data Platform engineering team while owning delivery of complex data initiatives. The role establishes delivery discipline, assesses technical and implementation risks, partners cross-functionally, and drives production reliability, data quality, observability, incident management, and on-call readiness. It also guides the evolution of scalable data ingestion, transformation, lakehouse, cloud architecture, governance, and reusable platform capabilities.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

Designs and operates batch, streaming, and real-time data platforms and pipelines using Spark, Kafka, Iceberg, Airflow, and cloud infrastructure. Owns data lake evolution, data quality, observability, reliability, governance, event instrumentation, schema management, and privacy controls for PII, retention, deletion, and residency. Leads complex initiatives end-to-end, troubleshoots production issues, mentors engineers, and develops AI agent harnesses that enforce data engineering standards and quality gates.

Designs and operates scalable batch and streaming data pipelines and AI-ready data products across Databricks, Snowflake, Kubernetes, and cloud services. Develops governed data assets, semantic models, search and retrieval capabilities, and data quality processes. Supports Snowflake Cortex and Databricks initiatives, production troubleshooting, orchestration, performance optimization, CI/CD, and observability. Partners with business and technical teams on roadmaps and adoption, evaluates AI-assisted engineering tools, mentors engineers, and establishes reusable platform patterns.