Top Data & Analytics Jobs

As a Snowflake Data Engineer, you will design and maintain scalable data pipelines, optimize SQL transformations, and support analytics platforms, collaborating with stakeholders.

Data Engineers at Rhombus Power design and implement data infrastructure, analyze and maintain data systems, and collaborate across teams. They develop code for data processes and ensure strong client connections.

Design and maintain scalable ETL/ELT pipelines, data integrations, APIs, databases, warehouses, and workflow orchestration. Improve data quality, security, governance, reporting accessibility, and infrastructure reliability across a multi-location organization. Collaborate with analysts, technology teams, and business leaders, troubleshoot complex data issues, support CI/CD deployments, and provide technical leadership and mentorship while shaping long-term data strategy.

Design, build, and operate large-scale Hadoop-based data pipelines and analytics platforms. Responsibilities include ingesting, transforming, and analyzing structured and unstructured data; developing Spark applications; managing streaming and distributed systems; orchestrating workflows; troubleshooting production platforms; and supporting analytics, machine learning, and reporting workloads. The role also involves improving reliability, performance, scalability, and cost efficiency across cloud and on-premises data environments.

Design, build, and maintain scalable ETL/ELT pipelines and data platforms. Lead cross-functional teams to ingest, parse, and process structured and unstructured data, develop scripts for data conversion, support operations and monitoring of secure information systems, and apply version control and agile practices to deliver analytics-ready data for mission-driven clients.

Design, build, test, and operationalize complex data pipelines and multi-layer data architectures using Snowflake and dbt. Analyze source systems, perform data mapping and transformations, ensure data governance, quality, and security, resolve defects, optimize query and pipeline performance, and collaborate with engineering and business teams to support BI and AI/ML use cases.

Lead end-to-end data architecture and engineering for scalable AI and analytics systems. Define standards for data modeling, ingestion, transformation, quality, governance, security, and observability. Mentor data engineers, lead complex projects, collaborate with AI/ML and software teams, translate stakeholder needs into technical designs, and drive data-driven innovation across the organization.

Leads the design and maintenance of automated data systems for insurance commission processing, accounts payable, payroll reconciliation, and agency payments. Builds ingestion, validation, normalization, reconciliation, mapping, encryption, and self-service tools. Manages agent and agency master data and cloud-hosted infrastructure with backup and recovery capabilities. Improves financial operations by replacing manual spreadsheet workflows with secure, scalable, auditable pipelines.

Define and lead enterprise data platform architecture across ingestion, storage, processing, governance, and consumption. Establish standards for modeling, schemas, streaming, security, lineage, and data products. Architect lakehouse and warehouse solutions, lead platform integrations, optimize costs, and design high-availability and disaster-recovery strategies. Partner with ML, BI, product, and engineering teams, guide architecture reviews, produce technical artifacts, and mentor data professionals.

Lead the architecture and technical standards for Curaleaf’s data and analytics ecosystem. Design secure, scalable Snowflake environments; establish SQL, Python, dbt, modeling, testing, CI/CD, and governance practices; and develop reusable data products and platform capabilities. Partner with engineering, analytics, data science, governance, and business teams, guide technical roadmaps, optimize performance and costs, lead incident reviews, and mentor engineers through cross-team technical influence without direct people management.

Lead design and implementation of enterprise-scale data architectures, pipelines, and integration frameworks supporting ontologies, knowledge graphs, and Semantic Web technologies. Build and optimize ETL/ELT processes for structured and unstructured data, enable cloud/containerized deployments, ensure data quality, lineage, governance, and support AI/ML initiatives. Provide technical leadership, documentation, automated monitoring, and mentorship to Government stakeholders and project teams.

Design and implement Palantir Foundry applications and data pipelines for defense clients. Ingest, transform, and model data; integrate AI/ML where applicable; implement CI/CD and DevSecOps practices; collaborate with stakeholders to translate requirements into technical solutions; support analytics, application, and workflow development in Foundry.

Design, build, optimize, and maintain large-scale data pipelines and analytics solutions. Analyze complex datasets, improve pipeline performance and cost, collaborate with cross-functional teams, and support the Insights product suite using cloud, Databricks, and modern data tooling.

Designs, builds, and operates scalable data platforms and ELT pipelines across Google Cloud using dbt, Python, Airflow, and BigQuery. Responsibilities include data modeling, governance, security, cost optimization, observability, infrastructure as code, incident response, and end-to-end feature delivery. The role pilots AI-assisted engineering practices, develops reusable tooling, collaborates with business and technical stakeholders, and mentors data engineers.

Design, implement, and maintain cloud-based data pipelines; integrate disparate data sources; optimize data lakes/warehouses and Spark workflows; automate Quote-to-Cash/Quote-to-Fulfillment processes; collaborate with platform, analytics, and business stakeholders to deliver scalable, reliable data solutions while ensuring data quality and security.

Design, build, and lead a scalable, configuration-driven data integration platform. Improve performance, automate monitoring and deployments, provide escalated support, and collaborate across product and engineering to deliver reliable, secure data integrations.

Design and build scalable pipelines and evaluation frameworks to compute driving-performance metrics across fleet and simulation data. Perform deep analysis of time-series, event, and geospatial data to identify regressions and root causes. Build geospatial analytics, release gating tooling, and LLM-powered agentic workflows to triage issues and propose fixes. Collaborate with engineering teams and adhere to QMS processes.

Design, build, and operate scalable batch and streaming data pipelines, orchestration workflows, governed datasets, and data products. Develop data models and analytics foundations supporting BI, AI, ML, and GenAI use cases. Partner with product, analytics, data science, AI, and business teams to translate requirements into production-ready solutions. Monitor and improve reliability, performance, quality, security, observability, and operational support across data and ML workflows.

The Data Engineer will develop and maintain AI context data capabilities, create data pipelines, ensure operational excellence, and follow compliance protocols. Responsibilities include data ingestion, retrieval quality improvements, and semantic layer implementation for AI integration within healthcare organizations.

Build, optimize, and operate the MIR Data Platform using high-performance relational data models, metadata-driven schemas, event-driven processing, reporting structures, and audit capabilities. Troubleshoot performance issues, blocking, deadlocks, and asynchronous processing failures. Support cloud migration to managed SQL platforms, production readiness, monitoring, HA/DR, incident resolution, and on-call coverage while collaborating with DBAs, SREs, architects, and platform teams.

Design and maintain scalable ETL/ELT pipelines, analytical dashboards, and reporting tools supporting clinical, research, and operational priorities. Write production-quality Python, R, and SQL; develop data architecture; partner with data scientists to operationalize analytics; integrate AI-enabled workflow improvements; establish engineering standards; and mentor junior team members.

Design, build, and maintain scalable data ingestion pipelines into BigQuery; replace legacy ETL/ELT; implement orchestration (Airflow/Dagster/Prefect); support DBT and CI/CD; tune BigQuery performance; set up monitoring/alerting; maintain documentation and data governance.

Design, build, and operate Databricks Lakehouse pipelines (Bronze/Silver/Gold) using Delta Lake and DLT. Optimize Spark/SQL workloads and Delta tables for performance and cost, implement incremental processing with exactly-once semantics, ensure data quality, and deliver documented datasets and dashboards (Power BI/other) with SLAs. Collaborate with cross-functional teams, integrate real-time sensor data, and support stakeholders. Occasional travel; primarily on-site.

Support building and testing digital solutions for production lines: analyze operational workload, assist with big data analytics and prototypes, troubleshoot production/maintenance tasks, and communicate insights under mentorship.

As a Senior Lead Data Engineer, you'll lead development teams, design technical solutions, utilize big data and cloud technologies, and drive transformation initiatives.