Top Data & Analytics Jobs

Build, test, deploy, and support reliable data pipelines serving client-facing websites and reports. Investigate data discrepancies, optimize data loading, improve data onboarding and integrity, and collaborate with global teams and business stakeholders. Participate in Agile delivery, release management, technical research, and proof-of-concept work. Provide reporting and data insights to senior partners.

Design, build, and operate big-data and streaming platforms (Kafka, Hadoop, Dremio). Develop, deploy, and monitor data pipelines using Java, Python, Spark/Flink; collaborate on data modeling and ingestion, ensure data integrity and availability, act as a Big Data SME, and perform root-cause analysis.

The Data Engineer II will build and optimize data pipelines, develop reusable data sets, and ensure data integrity and security.

Design, implement, and manage advanced data ecosystems and scalable ETL/ELT pipelines. Analyze complex problems, deliver data warehousing and governance solutions, mentor junior staff, and build client relationships while maintaining high standards and business-context awareness.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Builds and maintains scalable data systems, streaming and batch pipelines, data infrastructure, data models, and deployment pipelines. Develops data products using cloud technologies, improves data architectures, implements data formats and engineering frameworks, and partners with analytics teams to gather requirements. Requires programming and SQL expertise, Snowflake experience, and familiarity with DevOps, Terraform, and Docker.

The Data Engineer supports the data and analytics lifecycle by designing, developing, testing, and maintaining Big Data applications and pipelines. Responsibilities include building Spark and Scala processing logic, optimizing Hive and SQL queries, and maintaining integrations with HBase, Solr, JanusGraph, Kafka, and downstream systems. The role collaborates across business and technology teams, supports solution design and estimation, and develops skills through mentorship.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

Develop and maintain data pipelines and BI solutions for smart factory initiatives. Design data models, document data warehouses, build KPI dashboards, support ad-hoc analysis, and collaborate with stakeholders to enable data-driven decisions.

Configure and maintain Adobe Experience Platform and Real-Time CDP data pipelines, XDM schemas, datasets, identity resolution, Profile enablement, segmentation readiness, and destination activation. Validate data quality, troubleshoot ingestion and identity issues, manage sandboxes, and implement privacy, consent, governance, and access controls. Partner with architects, data engineers, consultants, analysts, data scientists, and marketing teams to support reporting, personalization, and machine learning readiness.

Design, develop, test, and maintain scalable data pipelines and architectures. Ensure data quality, lineage, governance, and security. Build automated testing/monitoring, optimize storage and query performance, update data models, and support data integration using SQL/NoSQL, cloud, and distributed processing frameworks.

Lead design, development, and operation of large-scale Spark-based data pipelines and lakehouse solutions. Write and review Python/PySpark and Spark SQL production code, optimize Spark workloads on EMR/Databricks, manage Apache Iceberg table operations, drive AI-assisted engineering adoption, automate reliability and CI/CD practices, and lead technical evaluations and communities of practice.

Design, build, and maintain scalable ETL/ELT pipelines and data warehouse architecture. Partner with cross-functional teams to define KPIs, ensure data quality and governance, own billing data infrastructure, and deliver dashboards and analytics for leadership while mentoring peers.

Build, model, and maintain scalable BigQuery-based data solutions on GCP. Implement performant data models, storage partitioning/clustering, ETL improvements, data validation, and documentation. Collaborate with architects, data scientists, and engineers to deliver governed, high-quality data for reporting and AI initiatives.

Design, build, and maintain scalable data pipelines and API integrations; implement data quality monitoring and alerting; troubleshoot data issues; collaborate with business stakeholders; support CI/CD processes and scale architecture for future needs.

Develop, curate, and publish authoritative geospatial data products in ArcGIS Portal; build Python data-processing pipelines; and create Tableau dashboards and web maps. Manage metadata, hosted feature layers, and data discoverability while partnering with mission users to transform large spatial and tabular datasets into operational visualizations and automated workflows.

Design and deliver enterprise-scale data models and analytics-ready datasets in Snowflake; build reliable ELT/ETL pipelines (batch and near-real-time); enforce data quality, observability, and secure handling of PII; optimize performance and publish curated data to downstream systems while collaborating with product, operations, and engineering teams.

Design and build data infrastructure, pipelines, and transformations using Azure Data Factory, Azure Databricks, and Snowflake. Develop data architecture and modeling, maintain data lakes/warehouses, ensure data quality and security, optimize performance, and collaborate with clients to deliver tailored data solutions and governance.

Lead design and implementation of scalable data infrastructure, pipelines, and transformations using cloud platforms. Build and optimize data warehouses (Snowflake, Databricks), orchestrate workflows (Airflow, Hadoop), ensure data quality and security, and coach teams to deliver analytics solutions that drive tax practice insights and client outcomes.

Lead design and implementation of data infrastructure, pipelines, and warehouses using Azure Data Factory, Databricks, and Snowflake. Manage data modeling, quality, security, and analytics frameworks. Lead client accounts, mentor teams, and drive innovation to deliver data-driven business insights.

Design, build, and support complex batch and streaming data pipelines from structured and unstructured sources. Collaborate with engineering and analytics to deliver accurate, performant data, automate processes, maintain dev/test/prod environments, and participate in on-call rotation.

Build, test, monitor, validate, and support scalable ETL/ELT data pipelines and validation frameworks in Azure/Databricks. Perform source-to-target validation, data profiling, reconciliation, anomaly detection, automation, and production support to ensure data quality and reliability for analytics and reporting.

The Principal Data Engineer will lead data engineering efforts on public cloud, design scalable solutions, build data pipelines, and drive deliverables with a high level of technical expertise in relevant technologies.

The Solutions Data Engineer will manage data integrations end-to-end, utilizing FloQast's Data Platform and Data Studio to connect source systems and transform data for reliable outputs across applications.

Leads the design and management of data engineering solutions, including pipelines, storage systems, data architecture, quality frameworks, APIs, and data extracts. Deploys solutions across Kubernetes, Teradata, AWS, and Databricks; selects storage technologies; maps data lineage; establishes QA standards; and collaborates cross-functionally to improve data quality, accessibility, and operational efficiency. Requires independent judgment and flexibility for nights, weekends, and variable schedules.