Data Engineering at the City is a unique opportunity for meaningful, technically challenging, and service-oriented work. The Lead Data Engineer / Integration Specialist will play a key role in designing, building, documenting, and improving enterprise data integrations across diverse City systems. This position will focus on complex integrations that require strong data analysis, software development practices, and reusable engineering patterns. The role will support integrations across systems of record, centralized data platforms, SaaS environments, departmental applications, APIs, file-based systems, and systems that may not provide native APIs or standardized data access methods. In those cases, the Lead Data Engineer / Integration Specialist may help design custom API access patterns, extraction strategies, data contracts, or other integration approaches that allow the City to share and reuse data in a reliable and maintainable way.The Lead Data Engineer / Integration Specialist will help standardize integration patterns, improve data consistency and performance, reduce one-off integration work, and strengthen the City’s ability to build scalable data pipelines and APIs. The position will support priority departmental and enterprise integration initiatives, while also contributing to the broader enterprise integration portfolio managed by CityGeo.We are looking for a candidate who is not only technically skilled, but also passionate about leveraging data to improve City operations and community life. This team is fundamental to the innovation of City services that directly affect residents. If you are eager to contribute to a team that values innovation, efficiency, service- oriented work, and data-driven decision-making, we encourage you to apply. Some prominent services the team is responsible for include DataBridge, OIT’s enterprise data warehouse, AIS, the City’s custom address information systems which supports public address search and internal geocoding, and Databridge-Airflow, a custom workflow generator and orchestrator for departmental integrations (https://metadata.phila.gov/) and open data publishing. Some examples of OIT’s commitment to serving the public through the effective and innovative integration of City open data include:https://Atlas.phila.govhttps://property.phila.govhttps://Openmaps.phila.govhttps://streetsmartphl.phila.gov/www.phila.gov/solarmapwww.phila.gov/stressmapEssential Functions· Design, develop, test, deploy, and maintain complex enterprise data pipelines and integrations.· Work with departments, product teams, vendors, and technical partners to understand how data iscreated, stored, updated, accessed, and used.· Design integrations that connect systems of record, centralized databases, SaaS platforms, APIs, file-based systems, and custom applications.· Develop robust ETL/ELT workflows using Python, SQL, Apache Airflow, DBT, Postgres, PostGIS, AWS,MuleSoft, and related tools.· Develop or support custom API, data access, or extraction patterns for systems that do not providenative APIs or standard integration methods.· Establish reusable integration templates, code patterns, data contracts, naming conventions, anddocumentation practices.· Improve data consistency, transformation logic, performance, reliability, and maintainability acrossenterprise integrations.· Support critical systems and platforms such as DataBridge, DataBridge-Airflow, AIS, open data publishingworkflows, enterprise APIs, and department-facing data products.· Collaborate with data architects, systems engineers, application teams, and department stakeholders toalign integration designs with business processes and enterprise data standards.· Contribute to data governance, metadata, master data management, data quality, and integrationstandardization efforts.· Participate in technical design reviews, code reviews, and integration planning to improvemaintainability, scalability, and quality.· Troubleshoot production data pipelines and integration issues, including failures related to source-system changes, schema drift, data quality, performance, scheduling, or access.· Write clear technical documentation for systems, pipelines, APIs, data models, integration logic, andoperational procedures.· Communicate complex technical concepts in understandable terms to technical and non-technicalstakeholders.Competencies, Knowledge, Skills and AbilitiesCompetencies:· Clear and concise written and verbal communication.· Strong documentation habits and ability to explain technical decisions.· Creative problem-solving and critical thinking.· Ability to work with data from diverse domains and systems.· Ability to manage multiple priorities and workflows simultaneously.· Ability to guide technical implementation in a collaborative team environment.· Ability to identify opportunities for standardization, reuse, automation, and continuous improvement. · Service-oriented approach to working with departments and stakeholders.Knowledge and Skills:· Advanced Python skills for data engineering, automation, API integration, and pipeline development.· Strong SQL skills and experience working with relational databases, preferably Postgres and PostGIS.· Experience designing, building, and maintaining ETL/ELT pipelines.· Experience with Apache Airflow, DBT, Git/GitHub, CI/CD practices, and modern data engineering workflows.· Experience integrating with REST APIs, SaaS systems, databases, file-based systems, and other data sources.· Familiarity with API design, data contracts, schema validation, and integration documentation. · Familiarity with Docker, Bash, and Linux command-line tools.· Familiarity with cloud services, preferably AWS.· Understanding of data governance, metadata, master data management, data quality, and data standardization concepts.· Familiarity with MuleSoft or other integration-platform technologies is preferred. · Experience working with spatial datasets, addressing data, or geospatial systems is a plus.· Experience working in a public-sector, civic technology, or large enterprise environment is a plus.Abilities:· To reason about, model, transform, and validate complex datasets. · To translate business processes and stakeholder needs into technical integration designs.· To build maintainable, reusable, and well-documented integration patterns. · To troubleshoot data pipeline, API, and database issues across multiple systems.· To maintain clean, secure, and reliable data environments.· To work effectively in a hybrid environment that includes cloud, on-premises, open source, custom, and commercial systems.· To contribute to shared engineering standards and improve team practices over time.
