Top Data & Analytics Jobs

Design and build IBM DataStage ETL jobs to extract, transform, and load data into 3NF and star schemas. Implement Slowly Changing Dimensions and Change Data Capture, write strong Oracle SQL, use QualityStage and parallel processing, create source-to-target mappings, write/execute test cases, and use Unix shell scripting. Collaborate with stakeholders and maintain data warehouse ETL processes.

Design, build and deliver SAP-originated and non‑SAP data in formats consumable by non‑SAP tools and external B2B partners using SAP BO/Data Services, BW/BI. Support ETL, integration and data delivery processes.

Lead design and build of a cloud-based big data analytics platform: implement analytics infrastructure, integrate streaming and batch data, develop distributed processing pipelines, manage predictive analytics models, and apply machine learning and Bayesian methods. Work involves Scala on OS X and AWS, Java and Clojure, Kafka/Samza, NoSQL, and following TDD, Agile, CI/CD practices.

Seeking four DataStage ETL developers with 2-5 years experience for a long-term (2-3 year) contract. Candidates will be screened via phone or Skype; internal candidates preferred.

Develop, deploy, and support large-scale distributed Big Data applications. Design multi-tier software, participate across the SDLC, perform database design and performance tuning, troubleshoot production issues, and collaborate with internal clients. Work primarily with Hadoop, Spark, Impala, Hive in Unix/Linux and Windows environments within financial/consumer banking contexts.

Develop, deploy, and support large-scale distributed Big Data applications. Design multi-tier software across the full SDLC, perform problem investigation and performance tuning, work in Unix/Windows environments, and collaborate with finance domain teams. Use Hadoop, Spark, Impala, Hive, batch and online processing, shell/Perl scripting, and ETL/job-scheduling tools as needed.

Develop and maintain big data solutions using Hadoop, HBase, MongoDB and Core Java on Linux. Implement and support scalable data processing pipelines; contribute to big data architecture and platform tasks.

Lead Data Engineer/ETL role requiring 5-10 years of Informatica ETL experience. Responsibilities focus on BDM, TDM, data quality, big data, data warehousing, and star schema design. Strong verbal and written communication and a technical bachelor’s degree required. Unix experience is a plus.

Design, build, and deploy an ETL-based transactional data system to read and manage information state and transmit data via web services. Implement solution from provided design, perform unit testing, deploy to development/test, and collaborate with QA and DPS to resolve defects.

Design, develop, and release data ingestion and streaming solutions using Big Data technologies (Flume, NiFi, Kafka, Spark/Storm). Collaborate with stakeholders and distributed teams, follow agile practices, and support data warehousing and reporting efforts.

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.

Develop ETL and data visualization solutions for a big data customer and partner experience transformation program. Build native Hadoop and Vertica ETL pipelines, collaborate with stakeholders, and support data integration and visualization deliverables for analytics teams.

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, 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.

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.

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.

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.