Top Data & Analytics Jobs

Design, build, and maintain data pipelines and models for hardware manufacturing, device traceability, yield, and supply chain. Integrate MES/ERP/IoT data into a lakehouse, ensure end-to-end data quality, security, and governance, and partner with Hardware, Operations, and Quality teams. Support lakehouse architecture (Iceberg, Databricks, dbt) and reduce single-point-of-failure risk for manufacturing data.

The Sr. Data Engineer will build and manage data pipelines for large datasets, collaborate with teams, and leverage cloud technologies for data processing.

Lead Data Engineer responsible for defining architecture, engineering standards, and best practices for the enterprise data platform, collaborating with distributed teams for modernization and scalability.

Design, build, and maintain scalable data pipelines, ETL/ELT processes, data lake and warehouse infrastructure, and cloud-based data platforms. Optimize systems for performance, reliability, security, and cost while implementing monitoring, governance, and data quality practices. Collaborate cross-functionally with data scientists, analysts, engineering, and product teams to deliver reliable data solutions and improve the data ecosystem roadmap.

Designs and maintains ETL pipelines, data architectures, databases, data models, and master data management solutions. Analyzes, profiles, cleans, and validates complex datasets; establishes data quality standards; and develops automated data management processes. Partners with architects, data stewards, and business stakeholders to improve data integrity and support business requirements. Communicates insights through reports and presentations while using cloud, database, streaming, CI/CD, and data modeling technologies.

Build and maintain data warehouses, pipelines, scalable data models, and data quality frameworks for Waymo commercialization. Translate business requirements into database models, transform raw data for reporting and insights, ensure PII and ACL compliance, document solutions, and collaborate with data producers and consumers. The role requires SQL expertise, database knowledge, and programming experience in Python, C++, or Java, with preferred experience in distributed data processing and cross-functional project leadership.

Designs and delivers a global analytics platform supporting reporting, visualizations, self-service analytics, and embedded data products. Responsibilities include data collection and processing, semantic modeling, warehouse and pipeline development, query optimization, governance, analytics enablement, and stakeholder collaboration. The role provides actionable insights from complex travel data and builds integrated analytics applications using cloud platforms and business intelligence tools.

Designs, builds, tests, and maintains software and data infrastructure for business-facing solutions. Develops CI/CD and DevOps practices, supports applications throughout their lifecycle, contributes to internal tools and standards, and collaborates directly with end users. Requires Python, SQL, large-scale data engineering experience, Azure and Databricks knowledge, and familiarity with Power BI, Copilot Studio, and AI tools.

Designs and maintains scalable data pipelines and platforms, develops ETL/ELT processes, and migrates heterogeneous external data into standardized enterprise models. The role includes data ingestion, transformation, quality validation, reconciliation, and billing-data standardization across systems. It collaborates with engineering, product, DevOps, and DBA teams, contributes to technical decisions, mentors engineers, and applies AI-assisted development tools. The position is based in Richmond, Virginia, with four days in office and one day remote.

Designs and maintains Snowflake-based cloud data pipelines and lake architectures for asset-based finance and mortgage servicing analytics. Leads legacy-system migrations, ETL/ELT development, data validation, reconciliation, and financial integrity controls. Translates business requirements into Jira epics and user stories, supports project planning, testing, cutover, and go-live readiness, and coordinates with business, finance, operations, and technical stakeholders.

Own Doctronic’s end-to-end data platform, including CDC pipelines from production databases into S3, Iceberg, and Snowflake; dbt transformations; orchestration; warehouse architecture; monitoring; and governed access to PHI. Establish trusted, documented data layers for analytics, finance, dashboards, and AI model training. Implement HIPAA-compliant access controls, anonymization, and deletion workflows while partnering autonomously with product, finance, marketing, partnerships, and AI teams.

Designs, builds, and maintains scalable AWS data pipelines, integrations, lakehouse infrastructure, and Airflow workflows. Processes commercial pharmaceutical data, supports pharma KPIs and analytics, and develops pipelines for AI, machine learning, generative AI, LLM, embeddings, and RAG applications. The role also includes legacy-system migration, pipeline monitoring and optimization, data quality assurance, and translating pharmaceutical business needs into reliable data solutions.

Design and implement end-to-end AI features, scalable data pipelines, ETL/ELT workflows, Generative AI and agentic systems, ML and BI solutions, and MLOps/LLMOps practices across AWS, Azure, and GCP. Apply RAG, embeddings, vector databases, security governance, evaluation, reliability, and guardrails. Own technical workstreams, mentor junior engineers, and promote software engineering best practices in consulting environments.

Design and scale Zip’s enterprise data and AI platform across batch, streaming, structured, and unstructured workloads. Build reliable pipelines, AI-ready data products, semantic models, vector and hybrid search, and RAG capabilities. Establish standards for modeling, testing, governance, security, observability, and automation. Partner with engineering, data science, product, and business teams while mentoring engineers and influencing platform architecture.

Own and scale automated patient-data ingestion and transformation pipelines from EMR systems into clinical-trial matching workflows. Design reliable asynchronous, queue-based, containerized architectures; manage schemas, validation, data quality, monitoring, and production issues. Partner with AI and product engineering teams on healthcare data foundations, APIs, scalability, and trustworthy clinician-facing workflows. Operate autonomously in a startup environment while applying strong systems thinking and pragmatic technical judgment.

Design, build, and maintain scalable, high-performance data pipelines, ingestion, transformation, and data export solutions. Implement data quality, testing, and monitoring, collaborate with product and stakeholders to build analytical data models, support production jobs, perform PoCs, and contribute to roadmap, standards, and documentation.

Designs and leads scalable real-time and batch data pipelines, data platforms, architectures, data products, and intelligent APIs. Ensures data quality, supports BI and AI/ML initiatives, and develops ingestion, processing, migration, and analytical solutions for big data, NoSQL, and data warehouse environments. Provides technical leadership, mentorship, code reviews, testing, CI/CD implementation, system monitoring, troubleshooting, and cross-functional translation of business requirements into reliable cloud-based solutions.

Design and build modern GCP lakehouse architectures, including Python and Spark pipelines, BigQuery ingestion, Kafka CDC, Delta Lake and Iceberg UniForm integrations, Delta Sharing endpoints, Snowflake and Databricks connectivity, data lineage, governed access controls, and semantic-layer alignment. Collaborate cross-functionally, troubleshoot full-stack issues, and deliver reusable data-sharing adapters and end-to-end data features.

Support SAP BRIM data analysis, migration, and development across SOM, Convergent Charging, Convergent Invoicing, and FI-CA. Design SAP BODS ETL workflows, configure EMIGALL migration objects, develop mappings and reconciliation reports, validate migrated data, troubleshoot issues, and support testing, cutover, production stabilization, documentation, governance, and stakeholder collaboration.

Build and lead complex data integration projects from client onboarding through ongoing platform usage. Analyze and resolve data discrepancies, troubleshoot SIS and SSO integrations, develop internal Retool applications, perform code reviews, prioritize backlogs, manage projects, and mentor teammates. The role requires advanced SQL, development expertise, technical leadership, and collaboration with internal stakeholders, with Ruby on Rails, AWS, Git, and K-12 education experience preferred or desired.

Build and own Snowflake data models and dbt pipelines for a Customer Data Platform supporting marketing segmentation and customer journeys. Integrate complex source systems, manage data architecture and governance, conduct data discovery, and translate business rules into scalable solutions. Collaborate with engineering, analytics, product, and business stakeholders; lead technical workshops; mentor junior engineers; and implement automated data quality testing, CI/CD workflows, and AI-augmented engineering practices.

Build and maintain scalable data infrastructure, pipelines, warehouses, and models on cloud platforms, primarily GCP. Develop batch and real-time ETL/ELT workflows using Python, SQL, and Airflow-based tools; implement data quality, governance, metadata, lineage, CI/CD, and automation. Support production machine learning environments, optimize pipeline performance, collaborate with architects and analytics teams, lead solution delivery, and mentor data engineers.

Designs, builds, optimizes, and tests Snowflake lakehouse architecture and ETL/ELT pipelines integrating Salesforce, marketing, and business data. Develops Bronze, Silver, and Gold data models, semantic layers, and datasets for Power BI, analytics, and AI initiatives. Implements data quality and governance procedures, maintains Git repositories, documents architecture, leads solution design, reviews code, mentors engineers, and communicates technical solutions to stakeholders.

Design, build, and operate scalable batch and streaming data pipelines and analytical data models. Lead data architecture across ingestion, transformation, storage, and serving layers while implementing testing, CI/CD, observability, quality, lineage, governance, and access controls. Integrate restaurant and enterprise data sources, optimize platform performance and cost, deliver production-ready datasets for BI and AI, mentor engineers, and collaborate with cross-functional technical stakeholders.

The Staff Data Reliability Engineer ensures data integrity, quality, freshness, and availability across enterprise pipelines. Responsibilities include defining data SLOs, implementing observability, building resilient Informatica and Python/PySpark pipelines, automating validation and recovery, eliminating operational toil, leading data incidents and postmortems, creating runbooks, and applying AIOps, machine learning, and DataOps practices to detect anomalies and self-heal data workflows.