Top Senior Data & Analytics Jobs

Build and operate Imagine Worldwide’s AWS- and ClickHouse-based analytics platform, including infrastructure, data lakes, Spark processing, ingestion pipelines, Airflow orchestration, dbt models, CI/CD, monitoring, security, backups, and cost optimization. Process billions of device events and integrate program context into reliable analytical datasets. The role also involves incident response, documentation, architectural contributions, and collaboration with globally distributed analytics and operations teams.

Supports quantitative investment research across equities, fixed income, and managed products. Analyzes performance, risk, peer groups, and market data; develops dashboards, datasets, reporting tools, validation controls, and automated processes. Partners with research teams to resolve data issues, communicate findings, improve analytical efficiency, and support investment due diligence. Requires a Series 7 license or the ability to obtain one within 120 days.

Build and maintain scalable data pipelines from financial data vendors into Snowflake and Databricks gold tables supporting machine learning. Lead end-to-end projects involving LLMs, RAG, and AI technologies; implement CI/CD, infrastructure-as-code, containerization, testing, monitoring, and cloud-native deployments. Collaborate with stakeholders, ensure data quality, mentor junior engineers, and travel up to 20% to support distributed teams.

The Senior Data Analyst translates commercial business needs into technical data specifications, writes complex SQL queries, extracts and validates large datasets, and automates workflows with Python. The role delivers accurate analytics and reporting, communicates findings to technical and non-technical stakeholders, supports pre-sales and RFP activities, manages competing requests, and collaborates with Operations, Strategy, and Data Engineering teams.

Own MISUMI’s Snowflake data platform, dbt project, and ELT pipelines. Administer security, access, performance, and cost controls; build trusted dimensional models; resolve pipeline and data-quality issues; manage GitHub workflows; translate business needs into data solutions; and promote disciplined AI-assisted engineering practices. Mentor analysts and engineers, lead technical reviews, maintain documentation and QMS processes, and support regulated data environments.

Own end-to-end data domains, designing and maintaining curated analytics models across supply chain, finance, and syndicated data. Lead dimensional modeling, data quality, SLAs, incident response, Redshift cost optimization, and migrations from spreadsheets to governed models. Resolve metric definitions, document technical decisions, partner with Data Engineering, shape backlogs, mentor analysts and engineers, conduct code reviews, and participate in hiring.

Lead enterprise data engineering efforts supporting defense missions. Responsibilities include prioritizing technical tasks, mentoring engineers, conducting code reviews, translating government requirements into scalable data architectures, developing Databricks ETL/ELT pipelines with Python, PySpark, and SQL, troubleshooting pipeline performance and reliability, and documenting systems. The role collaborates with government stakeholders, product owners, data scientists, analysts, and technical teams to deliver secure, high-throughput data platforms and actionable insights.

Build and maintain data products, dbt models, ELT pipelines, data warehouses, Looker explores, and visualizations. Drive analytics strategy, self-service BI, data governance, security, observability, lineage, and stewardship. Mentor engineers, apply software engineering practices, solve complex business problems, and collaborate cross-functionally. Lead AI-powered analytics improvements, including intelligent monitoring, automated data quality checks, responsible AI governance, and AI-assisted development.

Leads the data, analytics, and AI team; consolidates the company data warehouse; governs cross-system reporting and metric definitions; oversees pipelines, data quality, security, and upstream integrations; prioritizes the team backlog; manages and grows a distributed team; and delivers automation and AI tools. The role requires strong data engineering, analytics, BI, governance, business communication, and platform decision-making skills. Travel occurs approximately quarterly.

Designs and develops AWS data pipelines and automated document-processing workflows for structured and unstructured data. Builds chunking, embedding, vector indexing, semantic search, knowledge bases, and RAG integrations. Develops AI agents using Amazon Bedrock and Claude technologies, deploys them through AgentCore, and maintains GitHub Actions CI/CD pipelines. Optimizes systems for scalability, reliability, security, and performance. Requires remote work in western U.S. time zones and travel to client workshops or meetings.