Top Senior Data & Analytics Jobs

Own and evolve the end-to-end product analytics platform, including instrumentation, event collection, ingestion, raw data storage, data pipelines, analytical models, metrics, dashboards, quality monitoring, observability, lineage, and governance. Partner with software engineers to ensure analytics data is accurate, scalable, traceable, and reliable for business and product decision-making.

Administer and optimize SQL Server, AWS Aurora, PostgreSQL, and related database environments supporting federal workforce reporting. Responsibilities include performance tuning, backup and recovery, replication, data integrity, reporting support, incident troubleshooting, Tier 2/3 support, security controls, audit logging, vulnerability remediation, and technical documentation. The role requires prior Department of Labor IT program experience, federal database administration expertise, and eligibility for DOL Public Trust suitability.

Design and optimize enterprise data architectures and pipelines using SQL, Spark/PySpark, Python, Apache Iceberg, AWS, ClickHouse, and PostgreSQL. Build greenfield application and data solutions, optimize databases and Spark jobs, integrate data virtualization and governance tools, and support CI/CD and infrastructure-as-code practices. Experience with financial services workloads is valued, while telecommunications knowledge is preferred but not required.

Performs revenue, pricing, economic impact, competitive venue, KPI, forecasting, and statistical analyses supporting Events DC sales and business growth. Develops reports, recommendations, ROI and cost-benefit analyses, metrics, presentations, and requirements documentation. Partners with sales, convention management, corporate partnerships, sports, and entertainment leadership to optimize venue bookings, financial performance, and event business strategies across Washington, DC-area properties.

Build and govern an enterprise semantic layer that provides trusted, version-controlled metrics for reporting, operational systems, data science, and AI. Responsibilities include SQL-based data modeling, testing, monitoring, lineage, quality management, certification workflows, catalog integration, performance optimization, stakeholder coordination, and documentation. The role manages one direct report, supports governance roadmaps, researches emerging tooling, and drives adoption of certified enterprise metrics.

The description combines recruitment management, event planning, business development consulting, and iOS application development. Responsibilities include developing recruitment strategies, training recruiters, maintaining candidate databases, coordinating events, managing vendors and client relationships, developing sales pipelines, and designing, testing, updating, and maintaining iOS applications.

Own and operate the analytics platform: design and deploy the semantic/metrics layer, administer the data catalog and self-service BI tooling, optimize Amazon Redshift performance, ensure platform reliability/observability, manage security and access policies, and lead tool migrations and vendor evaluations to enable self-service analytics at scale.

Design and lead scalable data architecture and orchestration systems supporting AI infrastructure and digital marketing platforms. Build production-ready batch and streaming pipelines, transform structured and unstructured data, and integrate sources with Snowflake. Apply sharding, replication, indexing, normalization, and data governance best practices. Develop tools for data scientists and analysts, collaborate cross-functionally, document architectural solutions, and guide teams in delivering secure, reliable, production-ready AI data systems.

Own the architecture, development, operation, reliability, governance, and cost of an AWS-based enterprise data platform. Build pipelines and dimensional models using Python, SQL, dbt, Airflow, and PostgreSQL; manage BI reporting and data quality; define the technical data roadmap; partner with business stakeholders; and lead a distributed three-person engineering team while remaining hands-on in coding and technical reviews.

Supervise data engineers developing and maintaining enterprise data products for analytics, AI, machine learning, and business intelligence. Responsibilities include team leadership, contractor oversight, governance and change management, resource planning, vendor management, architecture road mapping, incident support, and employee coaching. The role also supports cloud and lakehouse implementations, data quality practices, Agile and DevOps processes, and operational efficiency across multiple concurrent projects.