Top Data Engineer Jobs

Lead design and scaling of ELT pipelines and analytics schemas on Snowflake. Build Looker views, CI/CD automation, and orchestration workflows (Prefect/Airflow). Ensure production reliability with monitoring (Datadog, Monte Carlo), partner with cross-functional teams, and provide technical leadership including code reviews, testing, and end-to-end ownership of data integrity and platform trustworthiness.

Design, implement, and administer operational data flows: ingest, clean, enrich, transform, secure, and move mission-critical data. Perform exploratory data analysis, troubleshooting, and collaborate with customers to ensure compliant, reliable data pipelines.

Build and operate scalable data ingestion, transformation, and connector frameworks; design and maintain a knowledge-graph-based data fabric; normalize and vectorize enterprise data for LLM/AI workflows; implement governance, lineage, access controls, and secure APIs to serve ML/agent pipelines.

Design, build, and support scalable data platforms and pipelines (ETL, warehousing, APIs) using DBT, Snowflake, and SQL. Collaborate with architecture, security, and application teams, build CI/CD, provide escalated on-call support, mentor staff, and deliver data solutions for the bank.

Design and maintain scalable ETL/ELT pipelines, develop data solutions using Databricks and Snowflake, and collaborate with cross-functional teams to ensure data quality and performance optimization.

Looking for a Data Engineer focused on data visualization with experience in handling real-world data and effective communication through data. Extra credit for skills in probabilistic classifiers and full-text search engines.

Serve as a senior technical lead for data engineering consulting: work with customers, design complex data models, write and review efficient code, resolve complex bugs, mentor engineers, lead cross-functional development, and plan/execute automated and performance tests to deliver high-quality data solutions.

Design, build, and optimize scalable Azure-based data pipelines and data warehousing solutions. Implement ETL/ELT, data cleansing, validation, batch and real-time processing, monitor and troubleshoot pipeline performance, ensure data governance and security, document data flows, and collaborate with analysts and stakeholders to deliver reliable analytics-ready data.

Design, develop, and manage data infrastructure for internal and client projects, focusing on large-scale data pipelines, APIs, and cloud deployments.

Designs, builds, and operates production data pipelines supporting AI-enabled engineering workflows. Owns data curation, indexing quality, pipeline reliability, schemas, governance, metadata, lineage, and data-quality controls. Partners with AI, IT, and engineering teams to integrate systems such as Windchill, Jira, GitLab, and AWS. Supports retrieval-augmented generation, agentic workflows, evaluation harnesses, and production scaling in a regulated medical-device R&D environment.

Leads enterprise data integration and engineering capabilities, including APIs, middleware, ELT pipelines, data governance, master data management, and cloud analytics platforms. Designs secure, compliant integrations across ERP, CRM, clinical, research, quality, finance, and commercial systems. Establishes architecture standards, data quality controls, monitoring, validation, and support processes while coordinating internal engineers, vendors, and business stakeholders. The role also provides technical leadership for trusted datasets, regulatory compliance, operational reliability, and commercialization readiness.

Leads data engineering strategy, architecture, delivery, support, and lifecycle management for secure, scalable cloud and hosted data solutions. Collaborates with technical and business stakeholders to deliver complex datasets, establish enterprise data standards, manage data engineering teams, and oversee maintenance, change management, capacity planning, business continuity, disaster recovery, and regulatory security requirements. The role also evaluates third-party tools and supports Azure DevOps, SDLC, CI/CD, and technical innovation.

Leads data engineering for Payment Operations monitoring transformation. Designs and maintains Snowflake pipelines, tables, stored procedures, monitoring scenarios, KPI infrastructure, and data quality controls. Serves as the Snowflake subject matter expert, documents architecture and operational processes, guides technical partners, and translates business needs into scalable data solutions. The role requires production data engineering experience, advanced SQL, Snowflake expertise, job scheduling, pipeline operations, and stakeholder communication.

Lead design and delivery of major Azure-based data systems and production-grade pipelines. Build semantic models, support BI and ML workloads, drive platform architecture and standards, mentor engineers, and solve performance, reliability, and scalability challenges across Fabric, Synapse, Power BI, and broader Azure services.

As a Data Engineer, you will design, build, and manage data pipelines, improve data architecture, ensure accuracy, and collaborate with various teams.

Serve as the technical owner for customer-facing data workflows and integrations: build pipelines, write SQL and Python scripts, deploy AI-powered workflows, manage APIs and Snowflake ingestion, and translate repeatable solutions into product requirements while supporting customer success and internal engineering.

Apply mathematical modeling, statistical analysis, and systems optimization to evaluate and improve NexusOne platform performance. Define and validate data requirements, analyze pipeline throughput and governance compliance, and present findings to stakeholders. Collaborate with engineering and delivery teams to modernize legacy systems (Spark/Airflow), enable AI pipelines, and recommend NexusOne configurations to meet performance, compliance, and AI readiness goals.

As a Senior Data Engineer, you will design, build, and maintain scalable data platforms, ETL pipelines, and support analytics and ML workflows, ensuring data quality and cross-functional collaboration.

The role involves designing and implementing ETL solutions for data integration, conducting testing, and collaborating on data models and architecture documents for a client.

The Senior Data Engineer will develop data engineering solutions, manage ETL workflows, and establish a new data practice while collaborating with stakeholders.

The Senior IT Big Data Developer will design and optimize data architectures, develop ETL/ELT pipelines, support data integration, and ensure compliance with data governance standards.

The AI-Native Data Platform Engineer will design data foundations for AI systems in wealth management, architecting data pipelines, models, and frameworks to enable autonomous financial agents.

The Data Architect will design scalable data models, transform raw data into actionable insights, and deploy analytics solutions for healthcare providers, with a strong emphasis on SQL proficiency and cloud technologies.

Design, build, and maintain scalable ETL/ELT data pipelines and analytical platforms on AWS and Databricks. Migrate legacy workflows to cloud-native services, implement data lake and catalog solutions, enable AI/RAG/vector search, ensure FedRAMP/NIST compliance, optimize performance, and mentor junior engineers while collaborating in Agile teams.

The Senior Data Engineer will design and operate the data platform, build ELT/ETL pipelines, optimize data performance, and ensure data quality while collaborating with business teams.