Top Data & Analytics Jobs

Serves as the bridge between retail business stakeholders and the enterprise data ecosystem. Builds Tableau dashboards and Snowflake SQL analyses, defines data requirements, oversees third-party data engineering pipelines, validates data quality, documents data flows, supports self-service reporting, and establishes BI governance standards. The role focuses on omnichannel retail systems including Shopify, NetSuite, Manhattan WMS, Klaviyo, and Google Analytics while ensuring privacy and regulatory compliance.

Administers the Raiser's Edge NXT donor database, ensuring data integrity, security, maintenance, reporting, integrations, and process improvement. Builds dashboards, reports, queries, and fundraising insights; trains staff; manages Blackbaud vendor support; coordinates system upgrades and integrations; and supports Finance with reconciliations, audits, and Form 990 reporting. The role also documents procedures, develops automation, and manages database requirements across departments.

Lead the data engineering foundation for MCG’s AI products. Build and operate data platforms, ingestion and transformation workflows, governed datasets, dashboards, and reliable production pipelines. Partner with Product, Engineering, Data Science, and customers to translate needs into technical solutions, support machine learning lifecycles, identify insights, and inform AI product decisions and roadmaps.

Design, build, and operate Databricks Lakehouse pipelines (Bronze/Silver/Gold) using Delta Lake and DLT. Optimize Spark/SQL workloads and Delta tables for performance and cost, implement incremental processing with exactly-once semantics, ensure data quality, and deliver documented datasets and dashboards (Power BI/other) with SLAs. Collaborate with cross-functional teams, integrate real-time sensor data, and support stakeholders. Occasional travel; primarily on-site.

Lead business intelligence strategy across product, clinical, commercial, finance, and engineering teams. Build measurement frameworks, semantic layers, reporting solutions, and visualizations that demonstrate business value and support data-informed decisions. Own the data lifecycle from ideation through client-facing delivery, ensure data governance and integrity, and translate complex datasets into actionable insights. Collaborate on scalable data architecture and apply statistical methods, advanced analytics, and emerging AI-driven BI capabilities.

Own an analytics project for go-to-market teams by combining CRM, campaign, and account data with SQL and Python. Build reusable data models, define acquisition and pipeline metrics, investigate data quality issues, add validation checks, and create dashboards and analyses. Communicate findings, recommendations, assumptions, and limitations to stakeholders while documenting reproducible workflows.

Develops and supports scalable Qlik Sense business intelligence solutions, including data models, load scripts, QVD layers, dashboards, reports, visualizations, and ETL processes. Partners with stakeholders to define reporting requirements, writes SQL, integrates data from multiple sources, optimizes performance, troubleshoots application and data issues, and supports Qlik governance and self-service analytics.

Build and maintain automated ETL/ELT data pipelines, improve data quality and monitoring, optimize Postgres and BigQuery schemas and queries, develop internal tooling, support data governance (HIPAA), participate in on-call rotation, and collaborate across engineering teams to scale data systems.

Analyze, clean, transform, validate, and document structured and unstructured datasets for federal analytics and AI-enabled systems. Maintain data pipelines, develop Power BI or similar dashboards, conduct exploratory analysis, identify data-quality issues, automate reporting workflows, and communicate findings to technical and non-technical stakeholders. Ensure data products meet federal standards for accuracy, governance, compliance, and usability.

Designs and maintains data architecture, canonical schemas, ETL/ELT pipelines, cloud data warehouse integrations, governance standards, data lineage, and documentation. Partners with engineering, product, business teams, and vendors to ensure reliable, trustworthy data. Designs data access patterns for AI and LLM applications, including retrieval-augmented generation and minimum-necessary-data principles.