Top Data Engineer Jobs in Philadelphia, PA

Design and build Power BI reports and dashboards across multiple data warehouses. Collaborate with business stakeholders and IT to gather requirements, develop technical specifications, and support data warehouse migrations through mapping, testing, and reporting modifications. Serve as primary contact for stakeholders and ensure production-quality reporting and documentation.

Lead design and implementation of data infrastructure, pipelines, and integrations using cloud platforms. Manage teams and client accounts, ensure data quality, security, and compliance, deploy scalable solutions (Databricks, Snowflake), mentor junior staff, and identify data-driven business opportunities.

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 will build and maintain data pipelines, optimize workflows, manage infrastructure, and enhance pipeline observability while collaborating with teams for effective data delivery.

Build and maintain reliable data pipelines, Airflow DAGs, and Snowflake-based Data Vault/warehouse models. Implement CI/CD, automated testing, observability, and production support while partnering with stakeholders and developing insurance domain expertise.

As a Data Engineer II, you will build, scale, and optimize data platforms, focusing on ETL/ELT pipelines, and collaborate with data scientists and AI engineers to support data-driven applications and insights.

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.

As a Data Engineer at Edmunds, you will architect and maintain data platforms for analytics and AI/ML, ensuring scalable, reliable data processing and supporting cross-functional collaboration.

The Data Engineer will help build modern data infrastructure, designing ETL pipelines, modeling datasets, and collaborating with teams for improved data quality and accessibility.

The AI Data Engineer will build data pipelines and semantic models, implementing Snowflake and dbt for AI products, focusing on data accuracy and usability for LLM tools.

Design, build, and maintain ETL pipelines and serverless AWS workflows for asset and cash-flow data; develop AI proof-of-concepts; integrate data sources and APIs; ensure security, monitoring, and production support; collaborate cross-functionally and maintain documentation.

Design, build, deploy, and maintain large-scale data pipelines and ETL using Python and Spark on Azure (Synapse/Databricks). Collaborate with cross-functional teams to translate requirements into data designs, implement CI/CD, monitor and troubleshoot production pipelines, ensure data security and governance, perform code reviews, and document solutions while driving automation and process improvements.

As the first in-house Data Engineer, you will own the data infrastructure, build and maintain data pipelines, and collaborate with teams to ensure quality data access and insights.

The Data Engineer is responsible for building and maintaining data pipelines, ensuring data quality and reliability, and collaborating with various teams on data needs. This involves working with AWS tools and programming in Python and SQL to support analytics and infrastructure.

Build and operate Vanguard's Microsoft Fabric data layer: design OneLake lakehouses/warehouses, pipelines, notebooks, and real-time ingestion; implement CI/CD, Terraform IaC, monitoring, governance, security, and cross-cloud integrations; serve as Tier-3 escalation and enable AI-ready semantic datasets for Power BI and agent consumption.

Lead architecture and implementation of scalable data pipelines, integrations, and cloud data infrastructure. Own IaC (Terraform), workflow orchestration (near-real-time and batch), third-party API and AI/LLM integrations, data export pipelines, and internal tooling. Mentor engineers, drive technical strategy and cross-team priorities, and communicate tradeoffs to technical and non-technical stakeholders. Must work EST/CST hours and be authorized to work in the US.

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.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

The Senior Staff Data Engineer will design scalable data systems, lead technical direction, optimize data platforms, and ensure data quality and performance for Observability & Insights.

The role involves designing, building, and maintaining ERP data pipelines, ensuring their reliability and supporting data integration initiatives. Responsibilities include troubleshooting, implementing SDLC best practices, and collaborating with stakeholders to optimize data-related outcomes.

The Looker Data Engineer will build and optimize data models, develop reporting solutions, and support data-driven decisions using Looker and other technologies.

The Data Engineer will design and maintain data pipelines, enhance the data warehouse, support machine learning applications, and collaborate with various teams to leverage data effectively.

The job involves leveraging AWS and Snowflake for data engineering tasks within a technology-focused company, requiring US residency.