Top Data & Analytics Jobs

The posting provides application instructions for an Enterprise Applications, Data and Analytics Manager role but does not describe specific responsibilities, qualifications, technologies, compensation, travel, or clearance requirements.

Design and develop scalable backend services, APIs, data ingestion solutions, ETL workflows, and data pipelines. Perform data modeling, mapping, and large-scale file manipulation while optimizing systems for speed and scalability. Collaborate in an Agile environment, apply LLMs to enrich data, and work with distributed systems, NoSQL databases, cloud platforms, and AI tooling. An active TS/SCI clearance with full-scope polygraph is required.

Lead end-to-end delivery of data products on Databricks, translating business requirements into technical designs, data models, builds, tests, and releases. Develop Bronze, Silver, and Gold-layer solutions using PySpark, Spark SQL, Delta Lake, Unity Catalog, Python, SQL, and Git-based CI/CD. Manage offshore engineering teams and vendors through backlog planning, design reviews, code reviews, and delivery oversight. Partner with business stakeholders on data definitions, quality, governance, and acceptance criteria.

Support database health, performance, availability, maintenance, troubleshooting, access management, documentation, SQL optimization, automation, migrations, replication, and disaster recovery testing. Collaborate with engineers and analysts while learning database performance tuning and operational best practices.

Analyze options trading data, identify actionable patterns, prototype strategies with traders, and build scripts, dashboards, and automated workflows. The role requires strong Python, statistical and numerical reasoning, SQL or willingness to learn, and familiarity with AI tools. The analyst will investigate market questions, test hypotheses, identify anomalies and operational improvements, and communicate findings that improve trading decisions and desk performance.

Organizes and analyzes construction and distribution data, improves data quality and workflows, supports standardized data structures and reporting, and identifies opportunities to automate manual processes. The role collaborates with cross-functional teams to implement practical digital solutions, develop dashboards, improve data accessibility, and support operational decision-making.

The Operational Reporting Manager develops and improves leadership reporting, analyzes operational trends and drivers, and translates business questions into actionable insights. The role partners with senior stakeholders across marketing, sales, practice management, and financial services; presents results to leadership; uses ThoughtSpot and existing analytics platforms; and identifies data issues and reporting requirements. Initially an individual contributor, the role is expected to grow into leading a small analytics and reporting team.

Design and operate a large-scale AI/ML data platform spanning ingestion, lakehouse storage, cataloging, governance, vector search, dataset versioning, feature and embedding pipelines, observability, and low-latency retrieval. The role owns production reliability and performance, establishes data standards, supports reproducible model training, manages petabyte-scale storage, and mentors engineers while leading technical architecture.

Manage and enhance foreign-language databases, data standards, training records, reporting, and presentations supporting USSOCOM’s LREC programs. Responsibilities include database design, troubleshooting, security, recovery, performance monitoring, system upgrades, application interfaces, disaster recovery planning, and customer support. The role also provides technical guidance, coordinates database projects, prepares briefings for senior leadership, and supports language proficiency testing and bonus programs.

Leads enterprise data warehousing, data engineering, governance, security, and AI-enabled data initiatives. Oversees AWS Redshift and Informatica-based platforms, ETL pipelines, data models, quality, lineage, metadata, testing, and regulatory compliance. Drives architecture modernization, production issue resolution, CI/CD integration, and responsible AI governance. Partners with executives, technology, risk, compliance, product, and business teams while managing vendors, budgets, and a large data engineering team.