Top Senior Data & Analytics Jobs

Design and govern scalable Azure-based data platforms and lakehouse architectures, lead data modeling (relational, dimensional, Data Vault), build high-throughput batch and streaming pipelines, enable data governance and security, integrate analytics/ML feature stores and LLM gateways, and partner with engineering and business teams to deliver production-ready data products and Power BI semantic layers.

Build and maintain data pipelines and ETLs using SQL and Python, create and update dbt data models, translate business needs to technical specs, deploy and test changes, monitor and improve pipelines, and support cross-functional reporting and analytics.

Lead development of enterprise data and analytics systems using Snowflake and Python. Build and optimize data pipelines on AWS (Lambda, Step Functions, ECS), write advanced SQL, and apply domain knowledge of asset management and financial markets.

Reverse-engineer Yardi schema and APIs to document finance entities and processes; reconstruct procure-to-pay and record-to-report flows; model flows into Gold-layer containers; produce source-to-target mappings and data-quality rules; define crosswalk keys with Procore and Oracle P6; steward finance KPI certification; support pipeline development, automated transformation testing, and reconciliation to Yardi system-of-record balances.

Lead liaison between business and technology for Data Services, translating strategic objectives into data, analytics, and reporting initiatives. Drive requirements gathering, solution design, prioritization, testing, delivery coordination, and adoption while partnering with Data Engineers, Architects, Governance, and Finance/HR stakeholders to ensure measurable business value.

Lead stakeholder engagement and translate strategic objectives into data and analytics initiatives. Define requirements, business rules, solution scope, and acceptance criteria; coordinate delivery, testing, deployment, and change management; support data governance and ensure solutions deliver measurable business value for sustainability and landfill operations.

As a Senior Data Scientist, you will analyze marketing strategies through predictive modeling and data analysis, and measure the impact of marketing investments on player behavior and retention. You'll collaborate with various teams to turn data into strategic insights, drive user acquisition and retention, and continuously improve marketing data usage.

Build instrumentation and batch/streaming ETL/ELT pipelines and REST API integrations to generate audience and trigger data for CRM/marketing automation. Ensure data quality, monitoring, and reliability at scale while collaborating with CRM, campaign, and operations teams.

Lead strategic analysis, scenario modeling, and executive reporting for Customer Office (Success, Renewals, Support, Services). Build financial and operational models, develop analytical frameworks and BI dashboards, manage planning and forecasting, drive performance management and data governance, and partner cross-functionally to inform executive decisions and operational improvements.

Lead and develop a team of data engineers while contributing hands-on to design, implement, and operate scalable, secure data pipelines. Partner with product, software, and architecture teams to deliver cloud-native data solutions, ensure production reliability, and drive engineering best practices and continuous improvement.

Lead design and delivery of scalable data pipelines and APIs for Digital Risk Protection. Mentor engineers, align and ingest multiple data streams (including graph data), productize analytics and LLM outputs, and drive DRP technical projects with stakeholders to enable data-driven intelligence.

Lead quantitative UX research for Target's enterprise products: own measurement programs (SUS, Voice of Vendor/Partner, intercepts, benchmarking), design and run large-scale surveys and experiments, perform statistical and driver analyses, develop dashboards and reusable measurement frameworks, partner with cross-functional teams to connect attitudinal and behavioral data, and translate quantitative findings into actionable product and business insights.