Top Data Engineer Jobs

Consult with business units to design, develop, and deploy high-quality data solutions. Query and process large datasets, integrate data into big-data systems, mentor engineers, perform quality testing, and collaborate closely with stakeholders in an Agile environment using SQL, Python, Spark, and cloud technologies (preferably AWS).

Build and maintain reliable Apache Airflow pipelines and workflow engines to move, schedule, and monitor data. Develop automation using Bash, Python, and Java. Work with distributed processing (Spark), containerization (Docker/containerd/Podman), and Git source control to support analytics and operational needs.

Lead the Financial Data Operations team owning architecture, maintenance, and improvement of critical financial data pipelines. Translate finance requirements into scalable solutions, drive adoption of Databricks/dbt/Dagster, build monitoring and QA automation, and use AI to streamline runbooks and troubleshooting while mentoring onshore/offshore engineers.

Design, build, and maintain ETL/ELT data pipelines and data models from SQL Server, PostgreSQL, SaaS, and internal apps. Ensure data quality, monitor ingestion, optimize Snowflake performance, collaborate with stakeholders to deliver dashboards and analytics, and recommend data architecture improvements.

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.

Lead the architecture and implementation of a new People Analytics platform on a modern data stack, ensuring AI readiness, governance, and security while optimizing for performance.

The Data Engineer will optimize data infrastructure, modernize SQL processes, support data governance, and mentor team members while enhancing analytics capabilities.

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.

Design, build, and maintain scalable ETL/data pipelines across distributed systems. Ensure data quality, integrity, security, and high availability. Monitor and optimize pipeline performance, perform root cause analysis, document systems, collaborate with global teams, and participate in on-call rotations to support production data systems.

Lead design and implementation of the enterprise data platform: architect scalable warehouse/lakehouse solutions, build and optimize ETL/ELT pipelines, enforce data quality and governance, tune analytical workloads, and enable reporting and self-service analytics in support of BI and AI initiatives.

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.

Lead architecture and hands-on engineering to design, scale, and govern a cloud-native lakehouse using Databricks and Microsoft Fabric. Build batch/streaming pipelines, ensure HIPAA-compliant PHI handling, produce analytics-ready datasets, mentor engineers, and drive platform strategy, governance, and reliability.

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.

As the Distinguished Architect, you will lead the design and implementation of CloudZero's next-generation data platform, focusing on streaming architecture, data modeling, and optimizing query engines to enhance cost attribution and performance at scale.

As a Senior Data Engineer, you will build complex data solutions and pipelines, support analytics growth, and educate users. Collaboration and advanced technical skills are essential.

Design, build, and operate production data pipelines across AWS, Databricks, and Snowflake. Modernize and migrate legacy AbInitio workloads, embed data quality/observability/lineage, optimize cost/performance, drive CI/CD and DataOps practices, integrate AI coding tools, mentor engineers, and standardize reusable pipeline frameworks.

Design, build, and iterate data engineering pipelines, dashboards, and reports to support analytics and data science. Partner with stakeholders to translate requirements into engineering solutions, promote self‑service analytics, define best practices, and support implementation using Databricks, Snowflake, and Azure components.

Design, implement, and maintain scalable ETL and real-time data pipelines using Apache NiFi; architect and optimize Snowflake data warehouses; administer Linux-based cloud environments (Azure/AWS/GCP); build CI/CD workflows; enforce data governance, lineage, and auditing; troubleshoot platform issues; mentor staff and document best practices to support micromobility and delivery analytics for the Department of Sustainable Delivery.

Design, build, and maintain cloud-based data pipelines and ETL processes to populate data warehouses. Optimize storage and performance, enforce security and data quality, automate tasks with scripting, mentor junior engineers, and evaluate new technologies to improve scalability and reliability.

Design, build, and maintain cloud-based data pipelines and ETL into data warehouses (Snowflake/AWS). Ensure data quality, security, and performance; optimize storage and retrieval; automate tasks with Python/Bash; monitor/troubleshoot pipelines; mentor junior engineers; evaluate new technologies and support legacy systems.

Design and build enterprise-scale Snowflake environments, develop and optimize data pipelines with Python, dbt, and Airflow, lead client technical strategy, implement Snowflake security/compliance, and mentor junior engineers while collaborating with Snowflake, AWS, and Anthropic partners.

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.