Top Data & Analytics Jobs

Owns the architecture, development, and support of data pipelines and integrations connecting Oracle Cloud Financials with enterprise data lakes, analytics platforms, Workday, banking systems, and third parties. Ensures financial data security, compliance, lineage, reporting reliability, and incident resolution. Leads Oracle Financials integration workstreams through implementation and hypercare, improves automation and performance, and provides technical mentorship and after-hours escalation support.

Leads data engineering, advanced analytics, and AI initiatives supporting engineering productivity and customer value. Translates engineering, manufacturing, and customer requirements into data tools, analytic workflows, visualizations, APIs, and scalable AI solutions. Oversees data monitoring, predictive and prescriptive analytics, agentic workflows, stakeholder engagement, agile delivery, and cross-functional projects while leading a small team and promoting documentation and knowledge sharing.

Build and operate scalable data pipelines and platforms that transform LiDAR, imagery, and GPS sensor data into reliable geospatial deliverables. Responsibilities include data ingestion, AI processing, quality monitoring, workflow automation, and AWS cost and performance optimization. The role partners with Data Operations, GIS, product, AI, and analytics teams and requires end-to-end ownership, including on-call support.

Owns customer-facing DataOps for mission-critical data pipelines, including deployment, monitoring, performance tuning, quality validation, ETL, metadata management, and troubleshooting across distributed messaging, orchestration, and cloud environments. The role coordinates flow owners, automates operational tasks, translates customer requirements into engineering work, and communicates platform status. It requires onsite work in Fort Meade, U.S. citizenship, an active Secret clearance, and eligibility for TS/SCI.

Designs and maintains data pipelines, data lakes, ETL processes, data integration workflows, and enterprise data platforms. Transforms and optimizes data using Databricks, Python, and SQL. Develops dashboards, reports, scorecards, and KPI solutions with Amazon QuickSight and Tableau. Works with PostgreSQL, Oracle, AWS RDS, and Starburst while ensuring data quality, performance, security, governance, and effective data modeling.

Build and maintain data pipelines for network and operational telemetry, integrate DataMiner and other monitoring systems, create Power BI dashboards, configure monitoring metrics and alerts, automate reporting, analyze network performance and recovery events, and translate technical findings into actionable insights for technical and executive stakeholders.

Designs, builds, and maintains scalable batch and streaming data pipelines across AWS and GCP. Develops ETL/ELT workflows using Python, PySpark, and advanced SQL; optimizes performance, storage, and query execution; implements data quality and governance controls; and supports data lakes, warehouses, and ML workflows. Collaborates with data scientists and ML engineers on MLOps automation and production deployments while maintaining CI/CD, Docker, infrastructure standards, and technical documentation.

Build and scale production data systems spanning laboratory, manufacturing, battery, and business data. Responsibilities include developing ELT pipelines, architecting secure AWS data movement, modeling warehouse data, optimizing SQL, managing infrastructure as code, implementing monitoring and data quality controls, and partnering with scientists and engineers. The role requires strong Python, SQL, cloud, software engineering, and end-to-end ownership skills.

Administer and optimize Oracle application databases while developing data models, warehouses, ETL pipelines, and analytics solutions across Oracle and Snowflake. Build scalable Python and SQL workflows, support Oracle Analytics Server, manage MongoDB use cases, automate monitoring, and implement CI/CD. Ensure data quality, governance, security, high availability, disaster recovery, and performance across enterprise data platforms.

Designs and delivers backend systems, APIs, data models, ETL workflows, data interactions, and user interfaces. Optimizes data operations for scalability, performance, integrity, and security while contributing to automated testing and CI/CD. Requires strong Python and Apache Spark experience, distributed computing knowledge, Agile experience, and data analysis skills. The role supports healthcare-focused digital services in a remote-first environment.

Designs and maintains reliable data pipelines and processing systems for high-volume telecommunications and digital evidence data. Builds data infrastructure supporting AI models and analytics, develops schemas and data models, optimizes performance and scalability, and monitors data quality. Collaborates with AI, data science, and software engineering teams, documents architecture, and follows secure practices for sensitive law enforcement data.

Build and maintain scalable data platforms, lakehouses, warehouses, ETL pipelines, Spark workflows, and AI/ML-integrated data solutions. Design dimensional models, implement governance and monitoring, optimize queries, integrate disparate data sources, and support KPI dashboards. Collaborate with engineering, product, DevOps, DBA, and security teams while mentoring engineers and analysts. Automate infrastructure and deployments using AWS, Terraform, and CI/CD practices.

Build and manage healthcare data and machine learning pipelines using Python and SQL. Responsibilities include feature engineering, data modeling, schema design, integrating new data sources, documenting and versioning code, scaling pipelines, managing client data SLAs, and collaborating with data scientists, project managers, clients, and healthcare stakeholders.

Build and operate data integrations, ETL pipelines, schemas, monitoring, and production services in a restricted, air-gapped government environment. Develop extensions, deploy software across low- and high-side systems, monitor data quality and pipeline health, troubleshoot incidents, perform root-cause analysis, and create runbooks. Serve as the on-site engineering liaison between the customer and Arlington-based engineering and DevSecOps teams. The role requires independent judgment, customer-facing field engineering experience, and operational ownership.

Lead Cohere’s Data Foundation pillar, managing a team responsible for the full data lifecycle. Own the roadmap, BigQuery warehouse, Airflow and Cloud Run orchestration, dbt modeling, Looker and API serving, Terraform infrastructure, governance, access controls, reliability, incident response, and data freshness SLAs. Lead development of an agentic analytics platform and evaluation framework, establish company-wide analytics standards, and partner with product, engineering, security, legal, and business teams.

Design and modernize scalable data engineering, business intelligence, and data warehouse solutions. Build enterprise ETL pipelines, support analytics and reporting, and leverage Azure, Databricks, and AI automation. Collaborate across teams to develop and test data warehouse applications, maintain quality through code reviews and ETL testing, troubleshoot data issues, and provide technical guidance to other Data Engineers.

Develop and optimize Oracle Fusion Data Intelligence analytics, reporting, semantic models, data integrations, and augmentation solutions. The role supports Oracle ERP, HCM, and SCM data, including OTBI, BI Publisher, ODI, ETL, SQL, and PL/SQL development. Responsibilities include requirements gathering, data cleansing and validation, cross-functional collaboration, and contributing to an ERP analytics roadmap. This is an onsite role in Des Moines, Las Vegas, or Portland, with an initial 3–4 month W2 engagement.

Design, build, and manage scalable batch and real-time data pipelines, databases, and processing systems. Develop and administer Databricks and Snowflake environments, optimize data architecture and distributed processing, manage PostgreSQL and MySQL databases, and implement governance, monitoring, alerting, and troubleshooting practices. Collaborate with analytics, engineering, and product teams to deliver reliable data solutions, document workflows, and promote modern data engineering practices.

Lead architecture and technical direction for critical data pipelines and domain data products. Design ingestion, transformation, modeling, and serving systems; establish standards for data quality, observability, testing, privacy, reliability, and cost optimization. Partner with product, analytics, and platform teams to deliver trusted datasets, mentor engineers, conduct reviews, evaluate technologies, manage technical debt, and communicate architectural decisions to stakeholders. Experience with distributed processing, multi-cloud platforms, batch and streaming pipelines, orchestration, and dimensional modeling is required.

Leads the design and development of scalable, secure data architectures and production data pipelines. Builds batch and streaming ETL/ELT solutions, manages data quality and reliability, applies cloud-native technologies, and ensures compliance with privacy, residency, security, and financial regulations. Partners with technical teams and business stakeholders, defines technical strategy, evaluates technologies, and delivers software solutions. Preferred responsibilities include mentoring engineers, leading technical initiatives, and applying AI/ML technologies.

Build and optimize Harrington’s Snowflake data warehouse and enterprise data platform. Design lakehouse architecture, medallion data layers, schemas, pipelines, SQL logic, and integrations with ERP and MDM systems. Support data migration, governance, lineage, access controls, documentation, vendor alignment, and cost and performance optimization. Mentor engineers and communicate technical decisions across technical and non-technical teams.

Architect and lead enterprise data platforms, pipelines, and infrastructure using Spark, Databricks, and cloud technologies. Build scalable data models and dimensional schemas, establish governance and data quality frameworks, optimize performance and reliability, and implement monitoring. Partner with stakeholders to translate requirements into technical solutions, lead design reviews, mentor engineers, contribute to platform strategy, troubleshoot complex failures, and support hiring and team development.

The Senior Data Engineer designs and manages data systems, warehouses, pipelines, APIs, and reporting solutions supporting Sales, Credit, Finance, and Operations. Responsibilities include gathering and validating data, automating reporting and analytics, optimizing SQL performance, integrating internal and external applications, translating business needs into technical solutions, managing vendor customizations, and mentoring IT team members. Commercial leasing and lending expertise is required.

Designs and maintains cloud-based data ingestion, discovery, and consumption pipelines across APIs, files, CDC, and streaming sources. Owns data-source onboarding, modeling, quality, privacy, lineage, testing, orchestration, monitoring, documentation, and production troubleshooting. Collaborates with product owners, engineering leads, and data stewards to build secure, governed Lakehouse data products. Applies software engineering, AI-assisted development, CI/CD, DevOps, and infrastructure-as-code practices.

Leads the architecture, development, and maintenance of scalable batch and real-time data pipelines, cloud data platforms, ETL/ELT workflows, and ML data infrastructure. Establishes standards for data quality, observability, lineage, governance, security, and modeling. Partners with data scientists, engineers, DevOps, and business stakeholders; evaluates technologies, drives technical initiatives, mentors engineers, and supports production-grade APIs and machine learning pipelines.