Top Senior Data & Analytics Jobs

Designs scalable data architectures, pipelines, schemas, and cloud-based storage solutions for high-volume structured and unstructured data. Ensures data quality, security, compliance, performance, and cost efficiency while applying AI and machine learning techniques. Builds enterprise AI agent orchestration systems using LangGraph and LangChain, partners with data scientists and stakeholders, develops reporting tools, and leads end-to-end technical projects.

Maintains data governance and quality across mission systems by validating sources, managing metadata and data dictionaries, reconciling metrics, performing profiling and cleansing, documenting lineage, and supporting cross-system harmonization. The analyst collaborates with reporting teams, dashboard developers, data owners, and PMO leadership to ensure accurate, standardized, compliant enterprise reporting.

Own product, content, subscription, and growth analytics for adult product lines. Analyze conversion, retention, churn, cohorts, and LTV; maintain reporting; create measurement plans; support experimentation; and deliver recommendations to Product, Engineering, Content, and Design. Use SQL, visualization tools, statistical methods, and AI-assisted workflows while validating results and documenting work.

Build and operate PharmaLink’s enterprise Databricks data, analytics, and AI platform. Develop scalable ETL/ELT pipelines, lakehouse architectures, curated data products, dashboards, AI applications, governance capabilities, and data-quality monitoring. Optimize Spark, SQL, pipeline performance, and costs while establishing standards for CI/CD, testing, observability, and production support. The role also requires troubleshooting complex data issues and partnering with governance, BI, application, and business teams.

Design and build scalable data infrastructure, including lakehouse architecture, ClickHouse deployments, ETL/ELT pipelines, and data models connecting Postgres with analytics systems. Establish data quality, observability, performance, cost, and reliability standards. Collaborate with product and engineering teams, define data contracts and specifications, support real-time analytics, and use AI-assisted coding and LLMs to accelerate pipeline development and transformation.

Designs and builds scalable data platforms and pipelines for customer and account data. Responsibilities include Snowflake warehouse development, dbt modeling, SQL transformation, data ingestion, testing, governance, quality controls, and performance tuning. Collaborates within agile teams and may develop Power BI reports. The role also supports data security, risk management, and cross-functional communication.

Own and evolve the Azure-based data platform for a large-scale telematics SaaS platform. Build high-throughput IoT ingestion, transformation, enrichment, storage, and analytics pipelines using Cosmos DB, Azure Data Explorer, Azure SQL, Fabric, Event Hubs, and Functions. Design scalable telemetry and multi-tenant data models, ensure pipeline reliability and observability, optimize costs and performance, implement governance and CI/CD, and collaborate with engineering, architecture, product, and leadership teams.

Leads data engineering, advanced analytics, and AI initiatives supporting engineering productivity and customer value. Translates engineering, manufacturing, and customer requirements into data tools, analytic workflows, visualizations, APIs, and scalable AI solutions. Oversees data monitoring, predictive and prescriptive analytics, agentic workflows, stakeholder engagement, agile delivery, and cross-functional projects while leading a small team and promoting documentation and knowledge sharing.

Design and govern the future-state enterprise data platform for the Medical Stop Loss business using Microsoft Fabric and Azure. Responsibilities include data architecture, dimensional and physical modeling, ingestion and integration patterns, data governance, quality, security, lineage, semantic models, and analytics enablement. The role guides data engineering and BI teams, modernizes legacy platforms, supports cloud migrations, and collaborates with business and technology stakeholders across regulated insurance operations.

Designs and operates data infrastructure for a clinical trial platform. Builds pipelines that extract, normalize, validate, and link information from clinical documents and publications into Aurora and GraphDB. Develops embeddings, vector search, RAG workflows, data-serving APIs, lineage tracking, and regulatory audit trails. The role also requires AWS-based orchestration, clinical data quality controls, biomedical knowledge graph modeling, and deployment automation using infrastructure-as-code and containerization.

Lead a 40-person data engineering organization at Slack, transforming its data platform for AI agents, real-time analytics, semantic data access, and data products. Own the engineering roadmap across infrastructure, ingestion, governance, tooling, and metrics layers. Establish reliability, freshness, and cost standards; partner with Data Science, Product, Infrastructure, and DevXP leaders; and hire and develop engineering managers and senior engineers. Drive MCP-based data access, AI-native observability, streaming infrastructure, and agentic analytics capabilities.