Top Data & Analytics Jobs

As a Data Engineer, you will design, implement, and maintain data systems for Notifications at Whatnot, ensuring data quality and performance while collaborating with cross-functional teams on data architecture and workflows.

The Data Engineer will support data initiatives, build data pipelines, perform statistical analyses, and develop BI dashboards to drive business decisions.

Build and maintain high-performance ETL/ELT pipelines and data schemas to connect engineering and operations data. Develop Protobuf models and gRPC microservices, administer and optimize Postgres databases, deploy data tools in Docker/Kubernetes, and implement automated data quality tests to ensure reliable, production-ready data for users.

The Data Engineer Senior role involves developing data solutions, leading analysis efforts, and designing data service infrastructures within financial services using various technologies.

The Data Engineer III will design, build, and optimize data pipelines and backend systems, integrating with SAP and supporting analytics applications. Responsibilities include developing ETL workflows, backend applications, and ensuring data quality.

Own and operate the end-to-end consumer data pipeline: ingest, clean, transform, and unify multi-source behavioral data into a deduplicated identity graph. Build and run batch and streaming pipelines, enforce data-quality and observability, support low-latency AI query access, perform root-cause incident analysis, and ensure privacy, compliance, and clear data lineage. Collaborate across product and fulfillment teams to maintain SLAs and document data contracts and architecture.

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, apply data modeling and DAGs, use Databricks, Airflow, and Hadoop, and collaborate with clients to deliver actionable insights.

Lead design and implementation of data infrastructure, pipelines, and integrations using cloud platforms. Manage teams and client accounts, ensure data quality, security, and compliance, deploy scalable solutions (Databricks, Snowflake), mentor junior staff, and identify data-driven business opportunities.

Design and maintain performance and advisor service metrics for OnStar features. Build dashboards and recurring reports using Databricks, SQL, Power BI/Tableau. Investigate end-to-end system performance, document workflows, identify gaps, and implement improvements. Collaborate with IT/operations, automate analysis tasks, and ensure SLA tracking and system requirement documentation.

The Data Engineer will design data models, build data warehouses and lakes, automate data pipelines, and enable data accessibility while maintaining security standards in a complex environment.

Lead technical strategy and architectural evolution in marketing technology, mentoring teams and driving innovative solutions across data engineering and platforms.

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

As a Data Engineer, you'll transform raw data into refined offerings, collaborate with stakeholders, build central logic layers, and leverage AI tools for efficient data workflows.

Design, build, and maintain enterprise ETL and data transformation pipelines to support Medicaid analytics and federal reporting. Optimize data processing with Python, Spark/Databricks, and relational platforms; ensure data validation, reconciliation, auditability, and production support. Collaborate across architects, analysts, QA, and BI teams during cloud migration and modernization efforts.

Lead Data Engineer builds and optimizes scalable data pipelines, implements robust data modeling and feature pipelines, enforces data quality/lineage, and provides tooling, runbooks, and enablement to drive predictive analytics and operational insights for technology leadership.

Lead Data Engineer responsible for designing, building, and optimizing data pipelines, feature stores, and robust data models. Enforce data quality, lineage, and observability, troubleshoot data/application issues, and enable teams via documentation, runbooks, workshops, and code examples to drive predictive and prescriptive analytics.

Lead hands-on data engineering to build and optimize scalable pipelines, feature stores, and data models; enforce data quality, lineage, and observability; support transition to predictive/prescriptive analytics; provide documentation, runbooks, enablement, and influence senior technology leadership through insights.

Design and maintain data pipelines, implement data integrations, mentor junior developers, and develop AI-powered solutions to enhance reporting and decision-making.

The Lead Data Engineer will develop full-stack solutions, collaborate within Agile teams, and work with big data technologies and cloud computing to improve predictive insights for decision making.

Belvedere Trading seeks a Senior Data Engineer to enhance trading performance through data availability and reliability, designing ETL pipelines and data architectures in a fast-paced environment.

Lead Data Engineer designs, develops, tests, and supports cloud-based, distributed data solutions. Collaborates across Agile teams, mentors engineers, works with big data, streaming, NoSQL and data warehouse technologies, and ensures performant, well-tested code for customer-facing products.

As a Lead Data Engineer, you will collaborate with Agile teams, develop technical solutions, and enhance cloud-based data warehousing to empower financial services for customers.

Design, build, test, and support cloud-based data platforms and real-time pipelines. Collaborate across Agile teams to deliver distributed microservices and data solutions using big data technologies, cloud data warehouses, streaming tools, and modern programming languages. Mentor peers, participate in code reviews, and ensure performant, well-tested systems that power customer experiences.

Execute end-to-end data migrations from legacy and competitor systems into Applied Systems SaaS products using SQL and ETL. Perform extracts, transformations, data cleanup, validation, and audits; develop repeatable scripts and workflows; collaborate with customers and cross-functional teams; support testing and defect resolution; leverage cloud platforms (BigQuery) and automation to improve migration efficiency and quality.

Design, build, and productionize batch and streaming ETL/ELT pipelines and ML workflows on Azure Databricks. Implement Unity Catalog data products, MLflow model lifecycle controls, CI/CD for data/ML artifacts, and operational monitoring. Partner with cross-functional teams to modernize legacy workflows into scalable, production-ready solutions.