Top Data & Analytics Jobs

Design, build, and maintain ELT and streaming data pipelines; operate Dagster/Airflow and dbt workflows; own Snowflake schemas and data quality; resolve data incidents; contribute to architecture, documentation, analytics tooling (Hex, AI agents), and participate in on-call rotation.

Serve as an experienced individual contributor and mentor on data engineering initiatives. Design and implement data architectures, ingestion pipelines, cloud databases, and visualizations. Lead smaller teams or projects, present technical designs to stakeholders, diagnose and resolve system issues, and help shape the program's data strategy and best practices.

Design and build cloud-native, scalable data solutions for systematic trading. Partner with traders, quants, and architects to develop Snowflake-based ETL, data models, and pipelines; lead AI-driven capability adoption; ensure security and SDLC best practices.

Design, build, and maintain integrations and reporting for the Ellucian Colleague ecosystem. Develop ETL pipelines, APIs/ web services, and data warehouse objects; support Colleague SaaS migration, troubleshoot integrations, ensure compliance reporting, and collaborate with functional stakeholders.

Lead design and implementation of a scalable enterprise data warehouse: dimensional modeling, ETL/ELT pipelines, cloud data platforms, data governance, performance tuning, monitoring, and mentoring. Collaborate with stakeholders and engineering teams to enable analytics, ensure data quality and security, and support data science/ML workloads including feature engineering and NLP components.

The Senior Data Engineer at BeyondTrust designs and manages data infrastructure, creates ETL pipelines, and optimizes database performance while ensuring data governance.

Founding data engineer to design and build Payabli's data platform: architect lakehouse/warehouse, build batch and streaming pipelines, model canonical datasets, ensure data quality/observability, enforce access/masking/lineage for regulated financial data, and enable analytics/ML feature pipelines while establishing team standards and CI/CD.

Design, build, and operate large-scale ETL pipelines to turn high-volume raw data into production-grade datasets for reporting and measurement. Own data products end-to-end from requirements through implementation, validation, and production rollout, collaborating with data science, backend/frontend engineers, and product. Ensure data quality, performance tuning, and operational reliability.

Design, build, and own large-scale ETL pipelines to produce production-grade analytics datasets. Translate requirements into specs, implement and validate data products, ensure data quality, debug distributed systems, and collaborate cross-functionally with data science, backend, frontend, and product teams for production rollouts.

Design, build, and operate large-scale ETL pipelines on Databricks/Delta Lake to produce curated analytics datasets. Collaborate with data science, backend/frontend engineers, and product from requirements through production, ensuring data quality, validation, performance tuning, and incident resolution.

Lead data engineering work across migrations, pipelines, integrations, and APIs. Design, build, deploy, test, monitor, and support production data solutions while ensuring data quality, validation, and adherence to architectural standards in an agile continuous-delivery environment.

Design, build, and maintain scalable ETL/ELT, batch and streaming data pipelines and platforms. Create reusable data models (dbt/Spark), ensure data quality and observability, collaborate with data scientists, analysts, and product teams, and mentor junior engineers while advancing data platform architecture and tooling.

As a Senior Data Engineer, you'll design and implement ETL/ELT pipelines, write Python code for data processing, and optimize SQL models. You'll collaborate with teams and improve data architectures to support AI and marketplace strategies.

As a Senior Data Engineer at Nimble, you will own the data platform, build and optimize data pipelines, ensure data compliance, and mentor team members, while collaborating across departments to enhance data accessibility and usage.

Build and operate large-scale backend data pipelines and ETL/ELT workflows for ingesting, validating, normalizing, enriching, and storing high-volume data. Implement Java-based services, batch and streaming processing (Beam, Spark, Dataflow), orchestration (Airflow), and cloud infrastructure (GCP). Improve data quality, observability, and reliability while collaborating with product and platform teams to deliver production-grade data acquisition solutions.

Lead design and delivery of scalable production data pipelines and data products (Spark/Python/SQL) on Databricks; integrate diverse federal health systems, define reusable ingestion patterns, ensure data quality/governance, mentor engineers, and troubleshoot/optimize production workflows.

Design, build, and operate end-to-end, production-grade data pipelines and lakehouse architecture using Python, Spark, and SQL. Enable AI/GenAI products by delivering AI-ready data products, defining platform standards, ensuring data governance and quality, and driving solutions from concept to production while partnering with product, data science, and enterprise IT teams.

Lead architecture and engineering of a Microsoft Fabric lake-house, build scalable data pipelines from 50+ sources, provide technical leadership, oversee vendors, implement governance/security, establish SLAs/monitoring, and develop semantic layers and Power BI datasets.

Designs and implements data quality monitoring systems, enforces data standards and policies, performs data quality assessments and root-cause analysis, develops data models and dictionaries, and monitors/remediates data defects across classified networks and enterprise systems.

Build and maintain scalable ETL/data pipelines and internal frameworks to support ML and product teams. Drive architecture, observability, data quality, and engineering best practices across AI/ML workflows and prompt-engineering pipelines.

The Staff Data Engineer will design and implement data architectures, manage partner integrations, build scalable data solutions, and mentor team members, ensuring efficient data operations and collaboration across teams.

Lead the design and implementation of scalable data architecture and engineering solutions, manage partner integration, mentor engineers, and ensure best practices in data systems.

Hands-on senior data engineer to design, build, and optimize Hadoop-based and real-time streaming data platforms (Kafka/Spark). Lead end-to-end delivery, integrate AI/ML and MLOps, enforce data security/governance in banking, mentor teammates, and improve performance and reliability of data pipelines.

Lead design, build, and operate scalable Databricks/Delta Lake data pipelines and Kimball dimensional models using dbt. Manage governance with Unity Catalog, optimize performance and cost, operationalize ML with MLflow, and coordinate/quality-check offshore vendor engineering. Mentor engineers, uphold standards, and deliver trusted data products that power analytics, reporting, and MLOps workflows.

Design, build, and optimize cloud-native batch and streaming data pipelines using Python and PySpark; lead SQL Server to PostgreSQL migrations; implement Terraform-managed AWS infrastructure; perform performance and cost optimization; develop data quality and monitoring frameworks; apply software engineering best practices and provide production support via an on-call rotation.