Top Senior Data Engineer Jobs

As a Lead Data Engineer, you will architect data foundations for AI applications, lead a team, optimize data pipelines, and ensure data governance.

Lead modernization and operation of Dutch Bros' data platform: design, build, and maintain scalable Snowflake-based data pipelines and infrastructure, enable real-time ETL/ELT, support analytics and ML workflows, implement CI/CD and IaC, ensure data governance and security, and communicate technical solutions to stakeholders.

Lead design, build, and maintenance of scalable data pipelines and lakehouse infrastructure using PySpark, Databricks, and Airflow. Own data & ML platform (Terraform, Databricks), improve observability, data quality, and developer tooling, and partner closely with Data Science and engineering teams.

The Senior Data Engineer will design and maintain data pipelines, write production-grade Python and SQL code, and support AI initiatives while collaborating across teams.

The Senior Data Engineer designs and optimizes scalable data pipelines using Azure services, collaborates with teams, mentors others, ensures data quality, and drives innovation in data architecture.

Design, build, and operate data services and pipelines to ingest, standardize, and deliver clinical data for product, analytics, and ML. Tune Spark workloads, enforce data quality, monitor production systems, and partner with product and engineering. Participate in on-call support.

The Senior Data Engineer will work with data tuning, big data technologies, and coordinate with technical teams and business users.

The Senior Data Engineer will work with data tuning on relational databases, utilize big data technologies, and coordinate with teams. A strong academic background and experience with large datasets are required.

The Sr. Data Engineer - Databricks designs and maintains scalable data solutions using Apache Spark and Databricks, ensuring data quality and performance for analytics and reporting.

Lead migration to a Databricks lakehouse, design and maintain dbt medallion pipelines, improve ingestion and reliability, implement data quality and governance, build curated datasets, mentor engineers, and drive platform standards and documentation.

Support and monitor data pipelines and curated datasets, perform exploratory analysis and model validation, develop SQL data models in Snowflake, troubleshoot ETL/ELT jobs, integrate proprietary and third-party data, validate AI-generated insights, and collaborate with stakeholders to deliver Customer 360 and leadership analytics.

Design, build, and maintain scalable ETL/ELT pipelines, integrations, and data warehouses (Snowflake, MySQL, SQL Server). Implement workflow orchestration (Airflow), data quality, security, and CI/CD. Partner with analysts and tech teams, troubleshoot systems, and provide technical leadership and mentorship to shape long-term data strategy.

Lead and deliver enterprise data programs: design Snowflake-based data solutions, data models, ETL and warehouse applications, provide architecture direction, drive technology transformation, collaborate with stakeholders, and apply advanced analytics (ML/AI/GenAI).

Design and implement scalable, production-quality data solutions using cloud-based big data technologies. Develop and optimize Spark/Hive workflows, write code in Scala/Python/Java, perform performance tuning, participate in SDLC and agile processes, and collaborate to identify and execute improvements.

Build and scale ad-tech data infrastructure and pipelines ingesting SSP/DSP/GAM sources, transform log-level and CRM data into analytics-ready datasets, maintain warehouses, implement data quality and privacy controls, and support BI and analytics teams for real-time campaign insights.

Design, build, and maintain end-to-end batch and streaming data pipelines on GCP. Migrate legacy warehouses to BigQuery/Bigtable/ClickHouse, orchestrate workflows in Airflow, ensure data quality and observability, and partner with ML engineers on feature pipelines, drift monitoring, and retraining.

Lead design and implementation of scalable GCP-based data pipelines and semantic layers for enterprise healthcare analytics. Drive ingestion automation, BigQuery optimization, ETL/ELT orchestration, observability, data quality, and metadata management. Provide technical leadership, mentor engineers, and collaborate with architects and stakeholders to deliver production-ready cloud data solutions.

Design, build, and maintain scalable cloud and hybrid data platforms, ETL/ELT pipelines, and data models. Implement CI/CD and DevOps practices, ensure data quality, security, and governance, optimize production pipelines, mentor junior engineers, and support data modernization and migration efforts for analytics and operational workloads.

Design, build, and operate large-scale batch and real-time data pipelines. Implement ingestion, transformation, orchestration, streaming, and downstream delivery using Kafka, Spark, cloud platforms, and workflow tools. Ensure reliability, scalability, performance, security, secrets management, and observability while collaborating with Agile teams to support enterprise analytics workloads.

Design, implement, and own Oracle data architecture and schemas for a cloud-native centralized data platform. Develop PL/SQL/SQL artifacts, optimize performance, enforce data standards, support ETL/CDC pipelines, ensure secure scalable database solutions, and collaborate with cross-functional teams to deliver mission-ready data products.

Own developer relations for GPU-accelerated analytical data processing: integrate NVIDIA libraries into query engines and OLAP databases, produce rigorous benchmarks, create reference integrations and guides, collaborate with engine architects and NVIDIA product teams to define APIs and roadmap, and drive adoption across open source and commercial projects.

Design and implement AI-optimized data platforms and production ETL/ELT pipelines, build semantic and analytical layers, write production-ready SQL/Python code, collaborate with stakeholders and analytics teams, and contribute to the firms data engineering practice and client delivery.

Lead and deliver AI-optimized data engineering solutions: design cloud data platforms, build ELT/ETL pipelines, implement semantic/analytic layers, write production SQL/Python/Spark code, apply AI-assisted tooling, and lead small teams on client engagements.

Design, build, and operate high-throughput real-time and batch data pipelines and analytics systems. Prototype and deploy data features, ensure data consistency, observability, and performance, and drive architecture and ownership while mentoring engineers and collaborating with product and infrastructure teams.

Build and maintain governed, activation-ready marketing data products in Snowflake. Design canonical marketing data models, event-streaming, identity resolution, quality checks, and reverse-ETL activation for marketing, CRM, paid media and AI use-cases. Enable predictive scoring, attribution, and AI-ready context layers while partnering with cross-functional stakeholders.