Top Senior Data Engineer Jobs

Lead development and delivery of data engineering solutions for clients: design data architectures, build and maintain ETL pipelines, apply advanced analytics, implement BI visualizations, coach teams, manage managed-services SLAs, and engage stakeholders to validate and refine outcomes.

Lead data engineering engagements delivering ETL/ELT solutions, pipelines, and cloud analytics. Build and maintain architectures (DataStage, AWS/Redshift, DB2/SQL Server, GoldenGate/CDC, S3/Glue), oversee BI and visualization, manage client accounts and teams, mentor junior staff, ensure project delivery and quality, and drive process and technology adoption.

Design, develop, and deploy AI-powered solutions and manage data infrastructure. Responsibilities include Snowflake administration, performance engineering, operations/on-call support, automation/DevOps, lifecycle management, security and compliance, monitoring, backup/recovery, incident/change management, SQL development, and hands-on Python coding.

Design, build, and maintain high-throughput data processing services, focusing on streaming and batch data systems to enhance player experiences.

The Senior Data Engineer will design and implement data architecture, manage production data pipelines, lead projects, and ensure quality and documentation while supporting progressive politics through data-driven solutions.

Lead and mentor a data engineering team to design, build, test, and deploy cloud-based, AI-enhanced risk management data solutions. Collaborate across Agile teams and product managers, implement big-data and streaming systems, perform unit tests and code reviews, and drive technology adoption and performance tuning.

The Senior Data Engineer will design and maintain data pipelines, build reusable data sets, validate data integrity, and ensure user privacy and security. This role requires strong expertise in SQL and Python, and familiarity with AWS and various data technologies.

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.

The Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

Build and own the companys modern data platform: ingestion, orchestration, Snowflake architecture, dbt transformations and CI/CD, observability, governance, and a semantic layer to enable trusted analytics and production AI/ML workloads. Partner cross-functionally and automate recurring operational pain points into reusable platform capabilities.

Lead technical strategy and architectural evolution in marketing technology, mentoring teams and driving innovative solutions across data engineering and platforms.

As a Senior Lead Data Engineer, you will design, develop, and support data solutions, optimize systems, and collaborate within Agile teams to leverage technology for business transformations.

Design, build, and maintain scalable Spark/SQL data pipelines and ETL/ELT frameworks for batch and real-time processing. Deploy and manage containerized data workloads on Docker/Kubernetes/OpenShift, manage data lakes on object storage (S3/ADLS) using formats like Apache Iceberg, implement CI/CD for data workflows, optimize Spark performance, drive architecture and modernization, ensure governance/compliance, and provide technical leadership and mentorship.

Lead design, build, and operation of scalable, secure GCP data platforms and reusable frameworks (ingestion, transformation, orchestration). Enable self-service analytics, enforce governance, data quality, observability, and security controls. Partner with domain teams, document patterns, and contribute to roadmap and tool selection.

Lead design, build, and operate scalable, secure GCP data platforms and reusable data frameworks (ingestion, transformation, orchestration). Enable self-service, governance, data quality, observability, and cost/performance optimization. Partner with domain teams, document runbooks, and contribute to platform roadmap and CI/CD integration.

Lead design, build, and operate reusable, secure, cloud-native data platforms on Google Cloud. Develop ingestion/transformation frameworks (batch/stream), orchestration (Composer/Workflows), shared libraries (Python/SQL/Beam), data modeling, governance-by-design, observability, cost/performance optimization, and enable self-service data consumption across teams.

As a Senior Data Engineer, you will implement data ingestion patterns, build pipelines in Azure, and enhance development with AI tools, aiding the migration to a cloud-based architecture.

As a Senior Data Engineer at Jellyfish, you'll build and maintain data pipelines, optimize orchestration, automate CI/CD processes, and enhance data integration while ensuring high performance and reliability.

The Senior Data Platform Engineer will design, maintain, and enhance Hinge's data platform, including data pipelines, monitoring systems, and user feedback incorporation while ensuring data quality and system reliability.

Lead design and operation of large-scale data pipelines and data APIs. Build Spark/PySpark workflows on Databricks, optimize job performance, manage data quality and observability, and develop MCP servers and AI-agent integrations. Mentor engineers, define standards, support production incidents and on-call rotations, and collaborate with stakeholders to deliver scalable data platform products.

The Lead Data Engineer will design, develop, and support complex technical solutions using Java, Python, AWS, and big data technologies. Responsibilities include collaborating with Agile teams, mentoring, and delivering cloud-based solutions.

The job involves leading a team to develop data-driven solutions, utilizing various programming languages and cloud technologies, while collaborating with Agile teams and mentoring junior members.

Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.

Consult on and design complex enterprise data ingestion, curation, provisioning, models, and architecture to enable analytics, reporting, and data science. Develop solutions, tooling, and process improvements, document systems, support implementations and transitions, and collaborate with teams and vendors. Provide observability-enabled application design and post-implementation support.

Lead architecture and reliability of the data platform, build and harden ingestion (batch and CDC), own BigQuery+dbt transformations, establish testing/observability/governance, mentor engineers, develop Python tooling and APIs, run POCs for lakehouse/open table formats, and define AI-assisted development practices.