Top Data & Analytics Jobs

Builds and maintains cloud data pipelines, transformations, semantic views, conformed dimensions, and analytical data models. Implements data quality controls, lineage, documentation, testing, release-readiness evidence, and defect resolution. Collaborates with data owners, governance teams, engineers, security stakeholders, data scientists, and customers to deliver reliable, governed data products supporting analytics, reporting, self-service BI, and AI/ML use cases.

Designs and optimizes scalable ELT/ETL pipelines using dbt, SQL, Snowflake, and Azure. Leads data architecture, dimensional modeling, performance tuning, CI/CD, data quality, observability, lineage, governance, and security. Partners with stakeholders to create reliable analytics assets, evaluates GenAI applications, mentors junior engineers, and supports modern cloud data operations.

Own developer relations for GPU-accelerated analytical data processing: integrate NVIDIA libraries into query engines and OLAP databases, produce rigorous benchmarks, create reference integrations and guides, collaborate with engine architects and NVIDIA product teams to define APIs and roadmap, and drive adoption across open source and commercial projects.

Build and lead NVIDIA’s enterprise data governance platform, covering sensitive-information detection, governed access, export-control enforcement, audit logging, data connectors, and AI knowledge readiness. Design scalable backend, distributed, and data-processing systems; integrate enterprise content sources; evaluate ML classification models; enforce access-control fidelity; and ensure ingestion quality. Provide technical leadership through architecture decisions, code and design reviews, mentorship, and cross-functional alignment with Security, Legal, Finance, and platform teams.

Develop and manage enterprise data platforms, pipelines, APIs, MDM/RDM solutions, governance frameworks, and AI-enabled observability for semiconductor supply chain data. Integrate SAP, PLM, planning, and business systems while building canonical models, metadata catalogs, lineage, data quality processes, and agentic AI workflows. Partner with engineering, business, and IT stakeholders to deliver scalable, governed data solutions across manufacturing and supply chain operations.

Build and operate production data pipelines and models for FinOps, including cloud billing, SaaS usage, cost allocation, forecasting, and optimization. Develop ingestion frameworks, data quality controls, lineage, monitoring, recovery automation, and scalable distributed processing. Partner with Finance and infrastructure teams, lead technical design and code reviews, mentor engineers, and ensure secure, auditable data operations.

Designs, builds, and scales secure enterprise data pipelines, warehouses, lakes, and lakehouses using Spark, Java/Scala, Hadoop, and AWS. Leads batch and near-real-time processing, cloud modernization, data governance, observability, production support, and technical initiatives across teams. Partners with stakeholders to deliver scalable solutions, troubleshoots incidents, establishes engineering standards, and mentors engineers while supporting Mastercard’s global payment platforms.

Design, build, and scale secure enterprise data pipelines, warehouses, lakes, and lakehouses using distributed computing and cloud technologies. Lead cloud modernization and workload migrations, implement data governance and observability capabilities, troubleshoot production issues, and deliver batch and near-real-time processing at petabyte scale. Collaborate with technical and business stakeholders, lead end-to-end engineering initiatives, establish architecture standards, and mentor engineers.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

Build and maintain large-scale data pipelines and analytics platforms for EDA datacenter observability, telemetry, reliability analysis, and capacity forecasting. Develop batch and streaming workflows, data models, quality frameworks, monitoring, and retention strategies using metrics, logs, traces, and hardware health data. Partner with observability engineers, data scientists, analysts, and infrastructure teams to deliver scalable, reliable, discoverable datasets for analytical and predictive use cases.

Designs and maintains scalable data pipelines, ETL/ELT processes, metadata repositories, data catalogs, and governance solutions. Responsibilities include data integration, modeling, lineage, quality monitoring, cloud data platform engineering, automation, and support for analytics and AI/ML workloads. Partners with architects, analysts, scientists, governance teams, and business stakeholders to improve data discoverability, compliance, and self-service analytics.

Leads enterprise data engineering strategy and architecture for pipelines, external data sharing, and scalable data platforms. Designs and codes critical data solutions while driving adoption of modern technologies, governance, resiliency, operational excellence, and unified data architecture. Advises stakeholders, mentors engineers, recruits talent, evaluates emerging data and AI technologies, and develops platform roadmaps. The role requires hands-on expertise across AWS, lakehouse architecture, streaming, distributed computing, data warehousing, and multiple programming languages.

Build and guide data platform capabilities supporting marketing, finance, analytics, and data science. Develop data collection, integration, scoring, experimentation, analytics, and infrastructure solutions; contribute code, lead cross-functional technical initiatives, improve system resiliency through incident response, and establish engineering standards. Partner with business and technical stakeholders to define strategy and long-term roadmaps.

Own Teal Health’s data warehouse, Fivetran ingestion, dbt transformation layer, and Omni semantic data models. Build reliable pipelines, integrations, tests, documentation, and layered warehouse models across marketing, product, operational, and healthcare data. Partner with Analytics on dashboards, establish data standards, troubleshoot source and schema issues, and manage ticket-driven priorities while protecting sensitive health data under HIPAA practices.

Designs, develops, and supports Hillwood’s enterprise Databricks data lakehouse, including scalable pipelines, medallion data models, CDC, Unity Catalog, governance, quality controls, monitoring, and CI/CD. Partners with business stakeholders to deliver trusted data for reporting, analytics, AI, and machine learning. Supports advanced capabilities such as feature stores, vector search, RAG, model serving, and structured/unstructured data integration while optimizing platform performance, reliability, security, and cost.

Builds business-critical dashboards, scorecards, reports, and ad hoc analyses using SQL, Python, Excel, Power BI, and Tableau. Analyzes KPIs, identifies trends and root causes, automates recurring reporting, validates data, and translates complex findings into actionable insights for stakeholders and leadership. The role collaborates with data engineers and cross-functional teams to improve reporting workflows, data pipelines, and decision-making.

Lead financial data engineering initiatives involving commissions, incentive programs, and backend financial systems. Build scalable, accurate, and reliable data pipelines, platform tooling, partner integrations, analytics infrastructure, and data science infrastructure. Shape technical strategy and roadmaps, collaborate with Finance, Product, Data Science, and Engineering, mentor engineers, contribute code, lead incident response, and establish engineering standards.

Lead the architecture and roadmap for Slack’s metrics platform, including ingestion, canonical datasets, semantic layers, APIs, and metric serving. Design scalable pipelines and data models processing billions of records with strong quality, reliability, freshness, and cost controls. Govern metric certification, lineage, and ownership while enabling people and AI agents to discover and query trusted metrics. Lead cross-functional technical initiatives, influence organization-wide standards, and mentor senior engineers.

Own and evolve UJET’s data platform, including MySQL CDC replication to BigQuery, dbt transformations, Terraform infrastructure, pipelines, data quality, observability, and Looker models. Build reliable, analytics-ready datasets and trusted metrics for internal and customer-facing reporting. Partner with Analytics, Finance, Product, and Engineering to deliver scalable data solutions, improve platform reliability, optimize performance and costs, and establish development standards and guardrails.

Design, build, deploy, and maintain scalable cloud-native data platforms, pipelines, lakes, warehouses, and analytics solutions. Support data science, AI/ML, and business intelligence initiatives while processing structured and unstructured data. Lead collaborative teams, guide analysts and developers, assess cloud architectures, and support migration and modernization efforts for mission-driven clients.

Design and build integrations connecting legacy practice management systems to an AI platform. Responsibilities include AI-assisted schema mapping, data modeling, ETL pipelines, REST/SOAP APIs, data cleansing, quality monitoring, production troubleshooting, documentation, and collaboration across architecture, product, engineering, and operations teams. The role requires applying LLM tools to schema inference, field reconciliation, anomaly detection, and data-quality automation while handling healthcare data securely.

Develop and maintain integrations, APIs, and ETL/ELT pipelines connecting internal and third-party platforms. Document data contracts, remediate legacy integrations, monitor pipeline health, troubleshoot failures, support new platform implementations, and enable governed business automations. The role also coordinates with data architecture, supports reporting and analytics, and contributes to modernizing the organization’s data platform.

Designs and supports enterprise-scale Azure data platforms, including batch, real-time, and event-driven pipelines, ETL/ELT solutions, data architectures, models, integrations, and storage. The role also handles governance, lineage, security, monitoring, troubleshooting, optimization, and production support while collaborating with technical and business stakeholders.

The Data Engineer, Senior will design and maintain data systems, build ETL pipelines, and collaborate with a team to optimize data solutions for client missions.

Designs, builds, and supports scalable data products, batch and streaming pipelines, ingestion and transformation systems using Microsoft Fabric and modern data engineering tools. Responsibilities include data modeling, quality monitoring, governance, performance optimization, CI/CD, observability, documentation, and production support. Collaborates with product managers, engineers, and analysts in an iterative environment while mentoring junior engineers and improving data infrastructure reliability, scalability, and cost efficiency.