Top Senior Data Engineer Jobs

Lead design and implementation of scalable data architectures and pipelines (real-time and batch), mentor junior engineers, ensure pipeline reliability and security, adopt new tools, collaborate cross-functionally, and implement data governance.

The Senior Data Engineer will design and maintain data architectures, work with operations teams, solve complex data challenges, and document decisions for stakeholders.

Lead design and implementation of scalable ETL/ELT pipelines and Snowflake data models. Optimize workflows, ensure data quality and observability, support BI (Tableau, Domo, Power BI), mentor engineers, and drive data platform architecture and roadmap.

The role involves building data pipelines, developing machine learning models, and improving actuarial and finance functions, collaborating with various teams.

Design, build, and operate a scalable, resilient Data Lakehouse. Lead architecture, evaluate open-source tooling, ensure performance and usability, mentor engineers, collaborate across teams, and drive DevOps and cloud best practices.

Founding technical leader for the data platform: build and operate ingestion, enrichment, batch and realtime pipelines; design storage, modeling, and governance; integrate LLM/ML workflows and vector search; hire and lead a small data team; and collaborate with engineering, product, and leadership to productionize data for valuations, analytics, and APIs.

Design and build Spark ETL pipelines on an AWS data platform, collaborate with stakeholders to deliver reliable large-scale data products, optimize and maintain data infrastructure, ensure data quality/governance, and manage platform operations and incident response.

As a Senior Data Engineer, you will design, build, and scale data pipelines and analytics capabilities, focusing on data modeling, ingestion, and automation using Python and PySpark in a healthcare context.

Own and evolve PayZen's data platform and architecture: design scalable warehouses/lakehouses and data models, improve pipelines and governance, enable analytics/AI/ML, and mentor the data engineering team.

Design, develop, and maintain AxiomSL data sources, mappings, and regulatory reports. Build scalable data pipelines, perform advanced SQL transformations, ensure data lineage and reconciliation, support audits and production L2/L3 operations, and drive automation and platform upgrades to meet regulatory requirements.

Lead design and delivery of scalable data integration, storage, and infrastructure to enable analytics and AI. Build ETL/ELT pipelines, cloud-native data systems, IaC and CI/CD, ensure SRE practices (SLOs/SLIs), prototype end-to-end solutions, and drive architectural trade-offs while collaborating across teams to transition validated solutions to platform engineering.

Own and evolve the data platform by leading architectural evolution, collaborating with teams for strategic data initiatives, and ensuring reliable data systems.

As the Senior Data Engineer for Enrichment, you'll design data pipelines, maintain contact datasets, ensure data quality, and optimize vendor relationships while working with a talented team.

Design, develop, and maintain scalable ETL/ELT and streaming data pipelines for terabyte-to-petabyte healthcare datasets. Build cloud-based data architectures, optimize queries and workloads, deploy containerized data applications, and collaborate with stakeholders to deliver analytics products and integrate data with consumer touchpoints.

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 Sr. Ab Initio Data Integration Engineer will maintain ETL processes, troubleshoot performance issues, and provide documentation for the data warehouse environment.

The Senior Data Engineer will improve data platforms, implement best practices, and lead initiatives across business teams using various cloud and big data technologies.

As a Senior Data Engineer, you will build data pipelines, improve the data engineering stack, and maintain a data warehouse for analytics. You'll collaborate with teams to ensure data accuracy and contribute to system reliability.

Lead design and implementation of scalable multimodal data pipelines, active learning and labeling loops, training and inference engineering, and model serving/optimization. Focus on data cleaning, OCR/layout normalization, sample construction, observability, and cost-efficient multi-model routing.

As a Senior Data & Analytics Engineer, you'll lead data initiatives, manage a team, and ensure data quality while turning insights into actionable decisions across the company.

Lead design and build scalable ETL pipelines and data models to support technology cost, usage, and finance insights. Partner with stakeholders, optimize performance, automate workflows, ensure data quality/governance, and mentor junior engineers.

Lead design, build, and scale of data platform capabilities across Snowflake and AWS. Develop ETL/ELT pipelines, CI/CD and IaC, monitoring, and governance. Mentor engineers, collaborate cross-functionally, and ensure security and compliance (SOC2, PCI-DSS).

Design, build, and maintain production data pipelines and the unified data model using Snowflake, dbt, and orchestration tools. Optimize, test, document, and monitor ELT workflows, enforce engineering best practices, mentor teammates, and deliver trustworthy data products for analytics and business decisions.

Architect, build, and maintain scalable ELT data pipelines and analytics-ready data marts. Lead dbt modeling, ensure data quality, CI/CD, and documentation. Partner with stakeholders, mentor junior engineers, evaluate tooling (reverse ETL), and drive long-term data platform strategy.

Design, build, and maintain production data pipelines and tooling (CI/CD, testing, validation) using Snowflake, dbt, and orchestration tools. Model, transform, document, and optimize data to produce reliable curated datasets for analytics and solutions, enforce engineering best practices, and mentor teammates while ensuring scalability, observability, and data integrity.