Top Data & Analytics Jobs

Design, develop, and support Big Data solutions on Teradata Aster and Hadoop platforms. Build ETL pipelines, data mining, reporting, and predictive analytics; write efficient code for large/unstructured datasets; translate business use cases to production; mentor users; contribute to Big Data roadmap and standards.

Develop and maintain Big Data applications using Java/Spring and Python on MapR HDFS with MapReduce, Pig, and Hive. Design NoSQL data models in HBase, participate in SDLC activities, configuration management, code reviews, and collaborate with offshore teams. Apply credit risk/fraud/payments domain knowledge and use tools like Jira, Maven, and SOAP UI.

Build quantitative analytics and electronic trading products by combining data analysis and full-stack web development. Develop dashboards and tools using ELK, JS frameworks, and SQL databases; write complex queries against Sybase/Sybase IQ/Rainstor and perform Excel-based analysis. Work in a small, entrepreneurial team to deliver production-ready analytics and web applications. C#/.NET and unstructured data exposure are nice-to-haves.

Own and evolve the data platform: architect, build, and maintain data models, pipelines, and access layers. Ensure data quality, governance, and reliability; monitor and optimize infrastructure; provide technical leadership, code reviews, and collaborate with cross-functional teams to deliver scalable data solutions for operations and analytics.

As a Senior Data Engineer, you will design and maintain data systems for Aircapture, focusing on building data pipelines and making architectural decisions to support climate technology.

In this role, the Senior Data Engineer designs and implements data architecture for analytics and ML initiatives, develops ETL/ELT pipelines, and ensures data quality and compliance, while mentoring junior team members.

The Sr. ETL Informatica Systems Engineer develops ETL applications for the EDW/BI program using Informatica tools, managing complex SQL and APIs.

Lead design, build, and maintain complex ETL/ELT data pipelines and integration solutions to support analytics and reporting. Ensure data quality, optimize processing for performance and scalability, monitor pipelines, document data lineage and procedures, and collaborate with stakeholders and analysts to resolve data issues and requirements.

Build and maintain scalable data pipelines and Python automations for ingesting, processing, and enriching data. Integrate SaaS sources via APIs, use Databricks/PySpark/Delta Lake and AI tools to power lead generation, enrichment, deduplication, and entity resolution. Collaborate cross-functionally to translate business needs into automated, production-ready solutions.

Design, build, and maintain production-grade data pipelines, Python services, and Databricks workloads for a large-scale financial data platform. Improve performance, reliability, observability, and data models; troubleshoot issues; participate in architecture, code review, and cross-team collaboration to deliver enterprise reporting and integrations.

The Senior Data Engineer will design and build scalable data platforms, handle automation for data operations, and ensure reliability and performance of data pipelines while collaborating with various teams.

Build and scale Allocate's AWS-based data lakehouse and pipelines (batch and real-time). Model financial entities and integrate internal/third-party sources. Enable ML/AI workflows, vector and knowledge-graph integrations, CI/CD and containerized deployments, and collaborate with engineering to deliver reliable, production-grade data and AI infrastructure.

The Senior Data Engineer will build and deploy data pipelines, develop warehouse architectures, and design analytics solutions for the team.

Own and harden Plot's core data platform: stabilize high-throughput ingestion, design read-optimized schemas and partitioning, separate transactional vs analytical workloads, ensure performant data access for dashboards and AI agents, write RFCs, and raise reliability and observability across teams.

As a Lead Data Engineer, you'll design scalable data frameworks, develop data pipelines, implement data quality checks, and collaborate with stakeholders to enhance data solutions.

Design and own end-to-end data domains and pipelines for plant sensors, lab results, and historians. Build schemas, ensure data quality, observability, and lineage, and supply model-ready time-series and analytical datasets to ML and operations teams. Mentor junior engineers and define data contracts.

The Data Solutions Engineer will design and deliver data and analytics solutions, manage data pipelines, and support cloud platforms while collaborating with various teams to improve data accessibility and operational efficiency.

Design, build, and maintain scalable data models, ETL/streaming pipelines, and cloud-native data platforms. Ensure data quality, governance, performance tuning, and enable ML workflows. Lead cross-functional teams, mentor engineers, and implement CI/CD, monitoring, and cost-optimized solutions to support analytics and real-time inference.

The Senior Data Engineer will design and manage data pipelines for massive datasets, ensuring quality, cost management, and effective data operations. Responsibilities include overseeing data flows, monitoring data quality, and managing vendor partnerships.

Design, build and maintain a self-service data platform to enable teams to create data products. Support real-time analytics, batch processing and ML workloads. Implement secure, scalable, compliant infrastructure with IaC and automation. Enable ingestion, processing and consumption of data at scale. Mentor teammates and drive engineering excellence.

The Principal Database Architect & Engineer will own the data components of complex infrastructure solutions and lead teams to deliver innovative database technology solutions, ensuring optimal performance and operational efficiency.

The Technology Analyst will develop Big Data applications leveraging Hadoop, Java, and Python. Responsibilities include project lifecycle participation, team collaboration, and client interfacing in a global delivery environment.

The role involves building scalable ELT/ETL pipelines on GCP, utilizing SQL and Python, and managing security and infrastructure automation while ensuring real-time processing and resource provisioning.

Design, implement, and maintain automated data testing and validation frameworks to ensure data integrity across ETL/ELT pipelines and reporting. Develop reconciliation, profiling, and automated test scripts; embed quality controls into Snowflake, Oracle, and AWS pipelines; support test environment strategy and governance; collaborate with engineers, QA, and stakeholders to improve DataOps, monitoring, and reporting validation while mentoring junior team members.

Build end-to-end data and analytics features: ingest and model data, build transformations and pipelines, create client-facing dashboards and interfaces, and develop LLM-powered workflows to accelerate analysis and app development. Partner with stakeholders, perform exploratory analyses, and ensure data quality, observability, and maintainability while influencing data engineering strategy.