Top Data & Analytics Jobs

Design, develop, maintain and test scalable big data solutions. Build preprocessing and reporting pipelines, collect/parse/analyze/visualize large datasets, test and deploy machine-learning models on big data, and work in agile teams to deliver analytics solutions.

Design, build, maintain, and test big data solutions and preprocessing/reporting pipelines. Collect, parse, manage, analyze, and visualize large datasets. Test and deploy machine learning models for scoring and prediction. Collaborate in agile teams to deliver scalable, reusable code and production-ready analytics on cloud platforms.

Design and develop Big Data solutions using Hadoop ecosystem (Pig, Hive, MapReduce, HBase, YARN, Spark, Impala, Kafka, Oozie). Implement Java and scripting-based pipelines, tune performance on large datasets, participate across SDLC phases, troubleshoot, and collaborate with stakeholders in a global delivery environment.

Design, develop, test and maintain scalable big-data solutions and workflows. Collect, parse, analyze and visualize large datasets; evaluate, test and deploy machine-learning models for scoring and prediction. Work in an agile team, automate tasks using scripting, and leverage Hadoop/Spark and AWS for production pipelines.

Design and develop Big Data solutions using the Hadoop ecosystem (Pig, Hive, MapReduce, HBase, Spark, Kafka, Oozie). Implement Java-based services and scripting, tune performance for large datasets, participate in full project lifecycle, and collaborate with stakeholders. Role requires extensive travel and strong communication.

Develop and maintain Hadoop-based big data solutions using Pig, Hive, MapReduce, HDFS, HBase, Spark, Kafka and related tools. Implement web services/APIs, performance tune large datasets, script in Java and shells, participate in full project lifecycle, and collaborate with stakeholders. Extensive travel and on-site work required.

Design, build, and maintain data warehouses, data marts, and OLAP solutions; develop ETL frameworks, data models, SSIS/SSAS/SSRS artifacts, and performance-tuned SQL Server databases to support analytics and visualization projects.

Provide technical leadership for the MFS data platform, collaborate with cross-functional engineering teams to improve platform adoption, mentor engineers, and design and develop large-scale distributed systems using Big Data technologies and Kafka.

Lead data discovery and design of enterprise data models, contracts, validation rules, and QA practices. Define metadata, data quality checks, and governance; partner with stakeholders and engineers to implement automated validation and ensure reusable, trustworthy city data.

Design, build, and maintain scalable data ingestion and ETL pipelines across a growing data lake. Define and enforce data quality standards, SLOs, and validation frameworks. Optimize pipelines for performance and cost, expand monitoring and alerting, and enable stakeholders to self-serve on data infrastructure.

The Senior Data Engineer designs and maintains data pipelines and architectures to support enterprise analytics, ensuring data quality and integration across systems.

The Data Operations Engineer will monitor and optimize trading processes, enhance integration and deployment pipelines, ensure quality control, and lead continuous improvement initiatives while documenting and reporting on production performance.

As a Data Scientist, you will drive data-informed decisions at Anthropic by analyzing product usage, conducting causal inference, and collaborating across teams to enhance developer platform performance.

Build and maintain scalable, reliable data pipelines and ingestion processes; support data integration, quality, governance, and cloud-based analytics platforms; troubleshoot partner/customer data environments; document standards and reference architectures; automate processes and ensure security and compliance for enterprise data solutions.

Manage, monitor, and optimize enterprise ETL/ELT data integration pipelines and platforms. Administer integration environments, implement observability and alerting, triage incidents (including 24/7 rotation), automate operations, maintain runbooks, and collaborate with cross-functional teams to ensure reliable data flow into the lakehouse and downstream applications.

Develop and implement a data management strategy, oversee data governance and architecture, ensure compliance, and manage vendor relationships to support organizational goals.

Design, build, test, and maintain high-quality data pipelines and storage systems. Collaborate with architects, analysts, scientists to enable data ingestion, transformation, real-time analytics, and data reliability. Write efficient code, unit/integration tests, and apply DevOps/SDLC practices while ensuring data security and cloud infrastructure best practices.

Lead design and delivery of scalable data platforms and pipelines on Google Cloud. Build and review Python/SQL code, drive data architecture using BigQuery and GCP tooling, own ingestion/transformation/orchestration, lead client technical discussions, prototype solutions, mentor engineers, and implement IaC, CI/CD, observability, and governance.

Design, build, and operate production-grade realtime and batch data pipelines and internal data products. Ensure data correctness, tooling for backfills and reports, and partner with product/business teams to prioritize data acquisition and improvements. Work across query engines and streaming frameworks to support analytics, monitoring, and debugging for product and business stakeholders.

Technical leader for Data Engineering responsible for designing, operating, and optimizing Snowflake-based data platforms, self-hosted orchestration (Prefect), dbt practices, AWS infrastructure, and data pipelines. Drive architecture, mentor engineers, partner cross-functionally, ensure observability and production-grade practices, and enable model-ready datasets and AI/LLM workflows.

Design, build, and operate controlled ETL/ELT pipelines and reconciliations between Delphi, ServiceNow, and PRISM. Maintain common-key mappings, enforce data-quality assertions, implement row/column access controls, manage exception queues, and produce documentation and runbooks enabling government staff to reproduce results independently.

Design, build, optimize, and modernize enterprise Microsoft data platforms (Azure, Fabric). Develop ETL/ELT pipelines, data warehouses/lakehouses, implement governance, security, DevOps/CI-CD, and support Power BI and AI initiatives while consulting with stakeholders.

Design, develop, and maintain large-scale data pipelines and ETL processes. Implement and optimize data storage and processing on BigQuery/GCP, ensure data quality and monitoring, participate in code reviews, mentor junior engineers, and help launch data-driven tools.

Design, build, and maintain data ingestion pipelines and integrations from intermittently-connected robots to analytics, ML training, and operations. Implement SDKs, connectors, schema contracts, retries, backfills, monitoring, and optimize pipeline performance, scalability, and cost. Collaborate with robotics, autonomy, and deployment teams to ensure reliable end-to-end data delivery and quality.

Design and build a scalable data platform and tooling for robotics/autonomy data ingestion, integration, and delivery. Build SDKs, connectors, and APIs (REST/gRPC, webhooks, CDC) to integrate diverse sources and downstream ML/BI systems, ensure schema/versioning/reliability, and optimize pipeline performance, scalability, and cost.