Top Senior Data Engineer Jobs

Build and scale Allocate's AWS-based data lakehouse and pipelines (batch and real-time). Model financial entities and integrate internal/third-party sources. Enable ML/AI workflows, vector and knowledge-graph integrations, CI/CD and containerized deployments, and collaborate with engineering to deliver reliable, production-grade data and AI infrastructure.

Lead architecture and implementation of enterprise-scale, production data pipelines and ecosystems for global, customer-facing products. Collaborate with product and engineering leadership, mentor peers, enforce code and CI/CD standards, and optimize data storage and performance for high-availability, defense-focused monitoring solutions.

Own and harden Plot's core data platform: stabilize high-throughput ingestion, design read-optimized schemas and partitioning, separate transactional vs analytical workloads, ensure performant data access for dashboards and AI agents, write RFCs, and raise reliability and observability across teams.

Design and own end-to-end data domains and pipelines for plant sensors, lab results, and historians. Build schemas, ensure data quality, observability, and lineage, and supply model-ready time-series and analytical datasets to ML and operations teams. Mentor junior engineers and define data contracts.

Design, build, and maintain scalable data models, ETL/streaming pipelines, and cloud-native data platforms. Ensure data quality, governance, performance tuning, and enable ML workflows. Lead cross-functional teams, mentor engineers, and implement CI/CD, monitoring, and cost-optimized solutions to support analytics and real-time inference.

The Senior Data Engineer will design and manage data pipelines for massive datasets, ensuring quality, cost management, and effective data operations. Responsibilities include overseeing data flows, monitoring data quality, and managing vendor partnerships.

The Principal Database Architect & Engineer will own the data components of complex infrastructure solutions and lead teams to deliver innovative database technology solutions, ensuring optimal performance and operational efficiency.

Design, build and maintain a self-service data platform to enable teams to create data products. Support real-time analytics, batch processing and ML workloads. Implement secure, scalable, compliant infrastructure with IaC and automation. Enable ingestion, processing and consumption of data at scale. Mentor teammates and drive engineering excellence.

The role involves building scalable ELT/ETL pipelines on GCP, utilizing SQL and Python, and managing security and infrastructure automation while ensuring real-time processing and resource provisioning.

Build end-to-end data and analytics features: ingest and model data, build transformations and pipelines, create client-facing dashboards and interfaces, and develop LLM-powered workflows to accelerate analysis and app development. Partner with stakeholders, perform exploratory analyses, and ensure data quality, observability, and maintainability while influencing data engineering strategy.

Lead architecture, implementation, and optimization of scalable data platforms, ETL/ELT pipelines, and data governance. Ensure security and compliance, mentor junior engineers, drive CI/CD and IaC for data workflows, and collaborate with stakeholders to deliver actionable analytics and reporting.

Design, build, and maintain scalable distributed data pipelines and a secure data lakehouse for streaming and batch processing to support real-time analytics, ML, and experimentation. Automate, test, and harden workflows, architect logical and physical data models, build ML model features, and collaborate with product, analytics, and data science teams to turn data into value.

Build and operate ingestion and transformation pipelines, model and test core warehouse data, enable analytics and reporting, and maintain ML data infrastructure feeding matching and recommendation models. Partner with Engineering, Product, and Data Science to ensure reliable, documented, and trusted data at scale.

Lead hands-on design, development, review, and optimization of production data pipelines and platform capabilities. Provide technical leadership, mentor engineers, drive CI/CD, observability, automation, and scalable data platform architecture to support analytics and ML workloads within an Agile environment.

Technical lead for data engineering who designs, builds, and owns scalable Snowflake ELT pipelines and data models. Mentors engineers, drives standards, optimizes performance and costs, and partners with product, analytics, QA, and governance to deliver observable, high-quality data solutions. Contributes to platform strategy, DataOps/CI/CD adoption, and ensures data quality, testing, and documentation.

Lead architecture and implementation of critical data pipelines and platform components, set technical standards, mentor engineers, own orchestration reliability, partner on roadmap, evaluate new tools, and ensure PHI compliance across data systems.

Design, architect, and build scalable data platforms and pipelines (Databricks on AWS). Implement ETL and API data interfaces, optimize data infrastructure and streaming (Kafka), and improve data quality and availability. Collaborate in an agile scrum team supporting a high-visibility public-facing product. Must be a U.S. citizen eligible for Public Trust clearance.

Design, build, and maintain scalable data infrastructure and automated pipelines. Develop data models, transformations, storage solutions, and quality/lineage checks. Troubleshoot pipelines, collaborate with analysts and security teams, document architecture, and support data governance and continuous improvement.

Design, build, and operate petabyte-scale data infrastructure: real-time ingestion, storage (open table formats), and serving. Architect scalable Iceberg-based data lakes, optimize Spark pipelines and streaming (Kafka/Flink), drive reliability, cost efficiency, and data contracts; collaborate with product and platform teams and establish best practices and tooling.

As a Senior Data Engineer, you will develop data pipelines, architect innovative data products, and integrate AI/ML models to enhance business insights.

The Senior Data Engineer will manage data tuning on relational databases, utilize Big Data technologies, and coordinate with technical teams and business users.

Lead the analytics data layer: design and operate a ClickHouse-backed warehouse, define and validate trusted metrics, build customer-facing React dashboards and internal Grafana/self-serve tooling, and ensure data quality, lineage, and documentation for telemetry-derived insights.

The Staff Data Engineer will build and optimize data pipelines, ensure data reliability, collaborate with teams, and shape the data engineering landscape at CookUnity.

Lead and architect large-scale data products and pipelines across teams. Design and deliver ELT/ETL solutions, evaluate tools, ensure scalability and integrity, influence technology decisions, and research emerging big-data/cloud methodologies (AWS, Hadoop/EMR, Spark, Kafka, Snowflake). Partner with architects and stakeholders to drive implementation and operationalize production workloads.

Own and improve the company data stack: expand the data warehouse, build and streamline Python-based ingestion and ETL processes, refactor dbt models, maintain data quality, standardize reporting, and collaborate with engineering and analysts. Act as technical owner for parts of the stack and proactively drive improvements and automation.