Top Senior Data Engineer Jobs

The Data Engineer Senior role involves developing data solutions, leading analysis efforts, and designing data service infrastructures within financial services using various technologies.

Own and operate the end-to-end consumer data pipeline: ingest, clean, transform, and unify multi-source behavioral data into a deduplicated identity graph. Build and run batch and streaming pipelines, enforce data-quality and observability, support low-latency AI query access, perform root-cause incident analysis, and ensure privacy, compliance, and clear data lineage. Collaborate across product and fulfillment teams to maintain SLAs and document data contracts and architecture.

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, apply data modeling and DAGs, use Databricks, Airflow, and Hadoop, and collaborate with clients to deliver actionable insights.

The Data Engineer III will design, build, and optimize data pipelines and backend systems, integrating with SAP and supporting analytics applications. Responsibilities include developing ETL workflows, backend applications, and ensuring data quality.

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

Senior Data Engineer on PwC's Managed Data, Analytics & Insights team to design, build and manage advanced data ecosystems. Responsibilities include designing data solutions and scalable pipelines, solving complex problems, mentoring junior staff, maintaining high delivery standards, and building client relationships while aligning solutions to business context.

The Data Engineer will architect and develop robust data frameworks for data platforms, focusing on data transformations and automated workflows. Responsibilities include collaborating with stakeholders, ensuring data quality, and maintaining high engineering standards.

Expand and optimize data pipelines for cross-functional teams, support developers and analysts, implement process improvements, and build analytical tools for business insights.

Lead analytics engineering initiatives, design data pipelines, develop business intelligence solutions, and ensure data governance while collaborating with stakeholders.

Design and operate scalable data systems for People Analytics, ensuring reliability and privacy of sensitive employee data. Collaborate with leadership to create data products for decision-making.

The Data Engineer will design and build scalable data systems, contribute to the architecture of the data platform, and collaborate with AI/ML engineers to enhance automation and intelligence in Canoe's systems.

Design, build, and operate big-data and streaming platforms (Kafka, Hadoop, Dremio). Develop, deploy, and monitor data pipelines using Java, Python, Spark/Flink; collaborate on data modeling and ingestion, ensure data integrity and availability, act as a Big Data SME, and perform root-cause analysis.

Lead design, build, and operate scalable AWS data solutions (ingestion, batch/streaming, transformation, modeling, warehouses/lakes). Collaborate with product and analytics teams, enforce data governance, debug production issues, and drive best practices, automation, and operational excellence.

Design, build, and maintain Databricks-based ETL/ELT pipelines and medallion-layer data products. Integrate enterprise applications (including DataStage and ServiceNow), implement data quality, governance, and operational support, and collaborate with cross-functional teams to deliver scalable, production-ready data assets for analytics, reporting, and AI.

As a Senior Associate in Data Engineering at PwC, you will design and implement data architecture strategies, analyze complex issues, mentor junior members, and build client relationships while ensuring adherence to project standards.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

Develop and maintain Python data pipelines to build business data objects. Implement source-to-target mappings as code, build and deploy CI/CD pipelines, perform unit and integration testing, resolve complex technical issues, ensure software quality, and collaborate across functions to support applications and new program development.

Own and modernize Upsides analytics data platform: migrate pipelines, reduce cost, improve governance, design reusable modeling/orchestration patterns, deliver domain-critical data products, lead cross-functional initiatives, mentor engineers, and support ML and product teams.

Design, build, and optimize SQL and NoSQL database solutions (PostgreSQL, Elasticsearch, DynamoDB). Develop stored procedures, functions, triggers, and complex queries. Implement CDC and AWS DMS, manage platform via GitHub/CI-CD and Terraform, monitor and tune performance, participate in Level 3 on-call, and collaborate with analysts, architects, and developers to deliver scalable data services.

Design, build, and maintain data architectures and pipelines to ensure data quality, lineage, and accessibility. Implement ingestion frameworks and transformations across cloud and on-prem platforms, enable data consumption (APIs, views, extracts), monitor data observability, and collaborate cross-functionally to meet privacy and business requirements. Work may include nights/weekends and requires independent technical judgment.

The Principal Data Engineer will lead data engineering efforts on public cloud, design scalable solutions, build data pipelines, and drive deliverables with a high level of technical expertise in relevant technologies.

Design and implement data pipelines, manage data with SQL and Python, automate workflows, ensure data integrity, and build scalable data solutions on AWS and Redshift.

Design and implement ETL pipelines, optimize data systems, build data warehouses, and develop AI/ML models for data solutions. Collaborate with teams to deliver data-driven products and ensure data quality and governance.

As a Data Analytics Engineer, you'll build and manage data models in Snowflake, structure datasets for optimal AI performance, and own data transformation while ensuring quality and accessibility for all teams.