Top Hybrid Data & Analytics Jobs

Designs and implements enterprise database architecture, relational databases, data warehouses, data models, archiving strategies, and migration solutions. Develops database security, backup, and recovery specifications; provides MIS and database operational support; codes utilities; resolves user issues; and supports legacy-system migrations. The role also requires collaboration, technical training, and expertise in Oracle, ETL, PL/SQL, and dimensional data modeling.

Lead and manage end-to-end analytics and visualization projects: design visual storytelling, build and maintain analytical models, automate reusable self-service tools and reports, mentor staff, establish best practices, and coordinate data acquisition, deployment, and retirement.

Lead development of customer-facing analytics and internal dashboards by building advanced SQL models, optimizing visualizations, and maintaining data models. Drive analytics priorities, improve team processes, present findings to leadership, and participate in agile planning and peer review to support product, operations, and commercial teams.

Build and lead Harvey’s central data platform, including streaming and batch ingestion, CDC, Snowflake warehousing, orchestration, transformation frameworks, real-time processing, self-service tooling, and data quality, lineage, and governance. Establish secure patterns for PII, access control, retention, and multi-region data residency while setting technical direction and mentoring a growing team.

Leads and coaches a data engineering team while contributing hands-on to platform architecture, data modeling, ETL pipelines, data quality, and analytics solutions. Designs trusted facts, dimensions, snapshots, and slowly changing dimensions using Snowflake, Databricks, dbt, and Airflow. Partners with stakeholders, security teams, vendors, and cross-functional groups on data strategy and controls, resolves production issues, supports incident response, and develops engineering talent.

Leads data engineering for a functional area by defining future-state architecture, establishing technical standards, mentoring team members, developing code and procedures, fixing bugs, maintaining quality, gathering requirements, and supporting application functionality. The role serves as a subject matter expert, partners with architecture and application engineering leaders, and drives technology adoption and professional development.

Owns marketing performance reporting, KPI frameworks, attribution, funnel measurement, and dashboard development across digital channels. Builds and integrates web, CRM, sales, and customer datasets using SQL, Databricks, BigQuery, and visualization tools. Analyzes user behavior, campaign performance, conversion, retention, and revenue influence to recommend personalization and optimization strategies. Partners cross-functionally with Marketing, Sales, IT, Finance, and Data teams while ensuring data quality, privacy compliance, and consistent tracking taxonomies.

Collects, cleans, validates, and analyzes government and industry data. Develops recurring reports, dashboards, visualizations, briefing materials, and analytical summaries. Maintains databases and data documentation, supports data quality and governance, responds to data requests, and coordinates with government personnel on reporting priorities.

Develop and maintain Power BI and web application dashboards, semantic models, and ad hoc analyses for internal and external stakeholders. Use SQL, DAX, dbt, Snowflake, and data modeling best practices to support scalable analytics and reporting. Collaborate with senior team members, contribute to version control and project management processes, manage multiple priorities, and build expertise in private markets and firmwide analytics.

Design, build, and optimize enterprise ETL/ELT pipelines, cloud data lakes, lakehouse solutions, data models, and semantic layers across AWS and Azure. Ensure data quality, security, performance, reliability, and governance while supporting analytics, reporting, and AI initiatives. Collaborate with product, engineering, IT, and business stakeholders to translate requirements into scalable data solutions, monitor pipeline health, troubleshoot issues, and improve data architecture and practices.