Top Data & Analytics Jobs

Design, build, maintain, and optimize Spark and Scala ETL/ELT pipelines on AWS for terabyte- and petabyte-scale datasets. Work across S3, Snowflake, Kafka, and monitoring systems, resolve production data-quality and performance issues, write automated tests, participate in architecture and code reviews, and support on-call operations. Use generative AI to improve pipeline development, monitoring, and engineering productivity.

Designs, develops, tests, deploys, and supports batch and streaming data pipelines using Azure and Databricks. Builds ETL/ELT processes, optimizes data workflows, supports cloud modernization, and performs monitoring, troubleshooting, and root cause analysis. Collaborates with business and technical teams on requirements, data governance, lineage, and audits. Mentors junior engineers and communicates solutions to technical and non-technical stakeholders.

Support enterprise data governance by curating metadata, managing business glossary terms, performing advanced data profiling/analysis, identifying anomalies, documenting issues, maintaining governance artifacts, and facilitating workshops to improve data quality and literacy.

The Data Analyst gathers, validates, and analyzes data from multiple sources to identify trends, evaluate KPIs, and generate actionable insights. They develop and maintain reports, dashboards, visualizations, and business intelligence solutions using tools such as Power BI. The role also involves partnering with stakeholders, translating business needs into analytical requirements, presenting findings to technical and non-technical audiences, documenting work, and improving reporting and analytical processes.

Designs and maintains Power BI dashboards and reports for sales decision-making. Extracts, integrates, cleans, and analyzes data from multiple market and category management sources. Conducts line reviews and category performance analyses, identifies trends and growth opportunities, and delivers data-driven recommendations. Develops customized dashboards, reporting materials, and PDF documentation to support sales teams, product evaluations, and customer presentations.

Collect, clean, transform, analyze, and maintain fund and CRM data related to venture and growth equity. Ensure data quality, manage databases and taxonomies, conduct statistical analysis, create dashboards and reports, and track market and competitive activity. Partner with attorneys and cross-functional teams to deliver insights supporting fund practice decisions and business development.

Analyzes autonomous-vehicle data and driving scenarios to validate dataset quality, identify edge cases and coverage gaps, monitor quality metrics, and investigate anomalies. Reviews annotation batches, maintains dashboards and reports, and automates validation and testing workflows using Python, Pandas, and ClickHouse. Collaborates with product and engineering teams to improve annotation efficiency, testing processes, and data quality.

Build and maintain scalable production data pipelines, infrastructure, warehouse models, analytics platforms, and data-quality systems. Partner with software engineers to instrument product features and ensure reliable event data. Support Hex and Snowflake self-serve analytics, maintain business metrics and data logic, and develop monitoring, validation, alerting, and observability tooling. The role also collaborates with product, analytics, marketing, and engineering teams, with potential future exposure to recommendation and LLM-based product work.

Designs and leads scalable real-time and batch data pipelines, data platforms, architectures, data products, and intelligent APIs. Ensures data quality, supports BI and AI/ML initiatives, and develops ingestion, processing, migration, and analytical solutions for big data, NoSQL, and data warehouse environments. Provides technical leadership, mentorship, code reviews, testing, CI/CD implementation, system monitoring, troubleshooting, and cross-functional translation of business requirements into reliable cloud-based solutions.

Own marketing measurement models for multiple customers, from configuration and QA through interpretation, updates, escalations, and action planning. Partner with strategy managers to translate causal MMM results into budgeting and channel decisions, protect revenue through high-quality deliverables, build scalable playbooks, supervise AI workflows, and provide customer feedback to product, science, and engineering teams.