Top Data & Analytics Jobs

Roku seeks a Senior Data Engineer to develop scalable data pipelines, optimize data quality, and maintain big data platforms, requiring extensive SQL and Python knowledge.

Designs and maintains financial data models, analytical pipelines, metrics, datasets, reports, dashboards, and self-service analytics tools. Performs statistical, predictive, exploratory, and financial analyses using large-scale cloud data platforms. Partners with stakeholders to translate business questions into analytical solutions, collaborates with engineering on data integration, and develops analytical code using Python, Java, SQL, and YAML.

Design and support ETL, data migration, transformation, import, export, and validation workflows for moving legacy EHR data into athenahealth systems. Build Python and SQL utilities, define source-to-target mappings, support HL7 and FHIR interoperability, validate EHI exports, investigate data quality issues, document processes, and collaborate with product, implementation, operations, sales, and engineering teams in a remote environment.

Build, test, deploy, and operate data pipelines and transformations for financial and economic datasets. Monitor production workflows, implement alerting, logging, automated testing, and data-quality checks, and conduct root-cause analysis. Partner with engineering, DevOps, application, investment, and research teams to deliver documented solutions. Analyze data to support investment-model research and automate manual processes, developing progressively greater ownership of private-markets data systems.

As a Data Engineer, you will design and implement data pipelines, analyze requirements, and develop software solutions to drive operational efficiency.

Design, develop, and optimize data warehousing and ETL/ELT solutions on Microsoft SQL Server and cloud platforms. Perform data profiling, cleansing, dimensional modeling (Kimball), and performance tuning. Build semantic models and Power BI/Microsoft Fabric solutions, advise on architecture, mentor teammates, assist in pre-sales/scoping, and promote BI best practices across clients.

Design and build large-scale ETL/ELT pipelines and analytical architectures on GCP. Assess, plan, and drive enterprise data and cloud migration programs, modernize legacy systems, optimize query performance, and enable scalable cloud-native solutions for clients.

Lead the architecture and evolution of enterprise-scale data platforms, applying distributed-systems and advanced data-modeling patterns. Establish technical standards across dbt, Airflow, Snowflake, Kafka, ETL, cloud infrastructure, security, monitoring, CI/CD, testing, and source control. Solve complex data problems, partner with senior leaders, provide technical governance, mentor engineers, and drive platform and process improvements.

Design, build, and operate scalable PostgreSQL databases and cloud-native data infrastructure. Build and maintain ETL/ELT pipelines, optimize query and server performance, ensure high availability and disaster recovery, improve observability, troubleshoot production issues, guide schema and application design, evaluate database technologies, and mentor junior engineers to support reliable, secure data-driven applications.

Lead pre-sales data engineering engagements to design and implement scalable lakehouse data platforms (Snowflake/Databricks). Advise customers on data strategy, governance, pipelines, AI-ready foundations, migrations, and translate technical solutions into business outcomes while supporting sales and partner enablement.

Architect and lead enterprise-scale data ecosystems and high-availability pipelines supporting global monitoring products. Build end-to-end solutions spanning architecture, ETL/ELT, orchestration, automated QA, CI/CD, and production integration. Establish data engineering standards, optimize existing systems, mentor team members, conduct design reviews, and partner with Product, Engineering, and commercial leaders. The role focuses on cloud-native, customer-facing data platforms, including geospatial datasets and defense and intelligence applications.

Design, build, and operate scalable batch and near-real-time data pipelines on AWS. Lead complex data engineering projects, translate business needs into data models and technical solutions, ensure data quality and reliability, and shape platform roadmaps. Integrate AI workflows such as LLM orchestration, embeddings, and vector search while collaborating with product and engineering teams across financial and analytical products.

Designs, builds, operates, and modernizes enterprise data pipelines, integrations, and Microsoft Fabric platform capabilities. Develops Python, PySpark, SQL, and Delta Lake solutions; supports ERP, CRM, dealer, manufacturing, and SaaS integrations; and maintains legacy ETL, stored procedures, middleware, and linked-server processes. Ensures reliability through testing, validation, monitoring, reconciliation, incident response, documentation, and CI/CD practices. Collaborates across technical and business teams while providing technical guidance and supporting critical production issues.

Build and manage high-volume analytics and production ETL/ELT systems, ensuring reliable data ingestion, quality, observability, security, compliance, and scalable performance. Partner with Data Science, Engineering, and Product teams to support accurate Search data logging and usage. Define data-system SLAs, optimize cost and performance, and support massive datasets, experimentation, and machine-learning applications.

Design and implement lakehouse data platforms using Microsoft Fabric and Databricks, build scalable ETL and ELT pipelines, develop enterprise semantic models, and deliver advanced Power BI dashboards. Translate client requirements into secure, performant analytics solutions while providing technical consulting, stakeholder guidance, training, documentation, and platform adoption support. Collaborate with project teams, contribute to data standards, and support business development through technical expertise.

Designs and operates scalable data infrastructure for high-volume batch and real-time workloads. Builds partner data-sharing platforms and identity graph foundations while improving data quality, lineage, observability, reliability, governance, privacy, and access control. Develops reusable tooling and frameworks for internal and external data consumers, supports secure and auditable data exchange, and partners cross-functionally on platform architecture, incident response, monitoring, and capacity planning.

Designs, builds, and leads enterprise data and AI platforms across Azure and Databricks. Develops scalable batch and streaming pipelines with Azure Data Factory, Databricks, and Apache Spark; implements DevSecOps, CI/CD, infrastructure as code, security, governance, data quality, and metadata standards. Supports analytics, machine learning, and AI workloads while mentoring engineering teams and partnering with stakeholders on enterprise data outcomes.

Design, develop, maintain, and test data infrastructures and pipelines; optimize data flow and collection; collaborate with data science to integrate models; analyze IT environments; deliver data solutions for stakeholders and support project milestones.

Own the go-to-market data infrastructure, integrating HubSpot, Dreamdata, advertising platforms, PostHog, and website data into BigQuery. Diagnose tracking and attribution discrepancies, configure multi-touch revenue attribution, and build executive dashboards covering channel performance and revenue. Develop ABM workflows using Clay, enrichment, behavioral signals, and AI personalization. Partner with sales and marketing to improve funnel visibility, campaign decisions, and outbound effectiveness.

Build and improve data pipelines, integrations, and AI agents for Starlink Enterprise. Clean and enrich CRM and go-to-market data, develop MCP servers and connectors, instrument data and agent performance, and partner with sales and ops to automate workflows and reduce manual cleanup.

Improve go-to-market data quality and build integrations and AI agents for Starlink Enterprise. Implement validation, enrichment, deduplication, MCP interfaces, and automations across CRM and operational systems. Instrument data and agent performance, partner with sales and ops, and ship reusable tools and monitoring to accelerate lead-to-revenue workflows.

Design, build, and maintain scalable Hadoop-based data processing pipelines and ETL for structured and unstructured data. Implement streaming and batch solutions (Spark, Storm, Kafka), integrate multiple data sources and NoSQL stores, optimize performance, define retention, and collaborate with product and data science teams in an Agile environment.

The Business Intelligence Engineer develops and maintains Power BI reports and dashboards, supports data validation, and collaborates with team members to ensure reporting accuracy and effectiveness. The role includes working with SQL for data retrieval and assisting in report enhancements.

Design, implement, and own reliable data solutions across design, manufacturing, and mission operations. Lead data architecture, migrations, ETL/replication, schema design, and optimizations. Partner with cross-functional teams, drive tradeoffs, prioritize work using data, mentor team members, and deliver production-ready data products.

The Data Engineer will design and implement data architecture, develop data ingestion pipelines, collaborate with stakeholders, and improve data-related processes.