Top Data & Analytics Jobs

Build and operate governed Finance data infrastructure on Databricks, including scalable pipelines, integrations, semantic layers, data-quality controls, observability, and AI-enabled services. Partner with analytics and enterprise teams on architecture, data contracts, security, recoverability, and production ownership. Optimize platform costs through FinOps practices and use AI coding assistants while validating performance, correctness, and security.

Leads a team of data scientists and BI engineers responsible for demand forecasting, customer routing analytics, budgeting projections, and core business KPIs. Designs scalable Snowflake datasets, metric definitions, and Tableau self-service analytics; partners with Product, Engineering, and Finance; establishes analytical strategy and roadmaps; applies statistical and causal reasoning; and guides responsible AI-assisted analytics practices.

Lead the strategy and governance for GTM analytics across pre-sales, post-sales, solutions engineering, forward-deployed engineering, and professional services. Establish trusted source-of-truth metrics, data models, semantic layers, self-service analytics, and AI-assisted querying. Partner with Data, Finance, Sales, and Services leaders to resolve metric definitions, improve data access, consolidate reporting, and guide cross-functional programs without direct authority.

Build and optimize enterprise data warehouses, data lakes, and scalable batch and streaming pipelines. Support Snowflake and Databricks ecosystems, resolve ETL/ELT bottlenecks, automate workflows with Airflow and Control-M, and manage AWS-based infrastructure. Develop production-grade Python, SQL, and shell solutions while applying DevOps, CI/CD, IaC, and data modeling practices. Lead incident management, root cause analysis, operational metrics, and Agile collaboration with data and platform teams.

Analyzes business and operational data using SQL and business intelligence tools to identify trends, root causes, and improvement opportunities. Builds dashboards and performance metrics, develops recommendations, communicates insights to stakeholders, and monitors outcomes following process or product changes. Ensures analytical outputs are accurate, validated, documented, and reproducible while collaborating cross-functionally to improve efficiency, customer experience, and business performance.

Design, build, and operate batch, streaming, and hybrid data pipelines in a regulated healthcare environment. Develop dbt models in BigQuery, ingest structured and unstructured clinical data, and implement data quality, lineage, governance, and compliance controls. Support ML workflows, CI/CD, monitoring, incident response, and platform architecture. Collaborate with data scientists, clinical, quality, security, and product teams while mentoring engineers and establishing standards.

Build and own end-to-end data products, including pipelines, transformations, semantic models, dashboards, metrics, and AI-enabled workflows. Partner with business and technical stakeholders to turn ambiguous questions into trusted, maintainable solutions. Establish data quality testing, documentation, monitoring, and governance while improving architecture and automation. Use Snowflake, dbt, Power BI, and related technologies to support analytics across Marketing, Product, Finance, and Engineering.

Own Hometap’s product and marketing event-data ecosystem, including taxonomy, tracking plans, governance, Segment Protocols, schema validation, and source management. Partner with marketing, product, and engineering to define instrumentation and event specifications. Build monitoring for schema conformance, event volume, and property completeness, while documenting standards and managing event review and deprecation processes.

Supports private-market asset data workflows by processing and reconciling data, monitoring exceptions, maintaining quality standards, analyzing trends, documenting operational logic, coordinating offshore teams, and partnering with Product, Data, and Engineering on process and product improvements.

Designs, develops, and operates scalable batch and streaming data pipelines and products. Integrates data from multiple sources, improves reliability and performance, establishes reusable engineering patterns, and supports AI, search, analytics, and machine learning workloads. Partners with technical and business stakeholders on architecture and requirements, while providing technical guidance and mentoring to less-experienced engineers.