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Fintech Programmer Jobs in Pennsylvania (NOW HIRING)

Monitise

Sr Quality Engineer

King Of Prussia, PA · On-site

$88K - $119K/yr

We're Fiserv, a global leader in Fintech and payments, and we move money and information in a way ... Job Title Sr Quality Engineer About your role: We're looking for a software test engineer or ...

100 Shift4 Payments, LLC

$140 - $180/hr

Lead Contact Center Technology Engineer This role is onsite and can be based at any of the ... Experience in FinTech, Payments, SaaS, or other highly regulated industries. * Experience ...

StackandCo

\n \n \n \n \n My client, a world renowned, fortune 500 fintech company are seeking a full stack developer to join their team. \n \n \n \n \n \n Role Overview The primary responsibility of the full ...

Wolfe, LLC

Software Security Engineer

Pittsburgh, PA · On-site

$110K - $120K/yr

Software Security Engineer Department: Security Employment Type: Full Time Location: Pittsburgh ... based FinTech company building the next generation of financial products, and we are actively ...

Wolfe, LLC

Software Security Engineer

Pittsburgh, PA

$110K - $120K/yr

Description About The Role Wolfe is a Pittsburgh-based FinTech company building the next generation ... As a Software/Application Security Engineer, you'll work hands-on alongside developers and DevOps ...

Xactus

Full Stack Engineer

Broomall, PA · On-site +1

The Full Stack Engineer is a hands-on software developer responsible for building, maintaining, and supporting fintech applications for XedaLink. This role works across a predominantly C#/.NET ...

Xactus

Test Automation Engineer

Broomall, PA · On-site +1

$45.75 - $60.50/hr

... fintech industry * Experience in Agile Scrum/Agile Development and automation testing methodologies with a focus on test execution, test planning, documenting test results, release and change ...

Xactus

Senior Full Stack Engineer

Broomall, PA · On-site +1

The Senior Full Stack Engineer is a senior individual contributor responsible for designing, developing, maintaining, and supporting business-critical fintech applications for XedaLink. This hands-on ...

Vestwell

Staff Software Engineer

King Of Prussia, PA

$185K - $200K/yr

The Engineering team is seeking an experienced Staff Software Engineer, with experience in Product ... Fintech experience * Startup experience * Building pipelines in Jenkins This role will be based in ...

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Fintech Programmer information

What is a fintech programmer?

Fintech Programmers are software developers who specialize in creating technological solutions for the financial industry. They design, build, and maintain applications related to banking, investments, payments, and other financial services, often using advanced programming languages and frameworks. These professionals work on projects such as mobile banking apps, trading platforms, blockchain technology, and financial data analysis tools. Their expertise helps financial institutions innovate, improve security, and enhance the customer experience in a rapidly evolving digital landscape.

What are some unique challenges fintech programmers face when developing financial applications?

Fintech Programmers often encounter challenges related to ensuring high security and compliance with strict financial regulations while developing applications. They must design systems that can handle sensitive financial data, prevent fraud, and meet industry standards such as PCI DSS or GDPR. Additionally, integrating with legacy banking systems and third-party APIs can be complex, requiring robust error handling and real-time data processing skills. Collaboration with compliance officers, product managers, and QA testers is essential to deliver reliable, secure fintech solutions.

What are the key skills and qualifications needed to thrive as a fintech programmer, and why are they important?

To thrive as a Fintech Programmer, you need strong programming skills in languages like Python, Java, or C++, along with a solid understanding of financial systems and algorithms, often supported by a degree in computer science or a related field. Familiarity with financial APIs, blockchain technology, cloud platforms, and tools like SQL databases is typically required, and certifications in areas such as AWS or cybersecurity can be beneficial. Attention to detail, problem-solving ability, and effective communication are vital soft skills for collaborating with cross-functional teams and adapting to regulatory changes. These skills ensure the secure, efficient, and innovative development of financial technology solutions in a rapidly evolving industry.

What is the difference between Fintech Programmer vs Fintech Software Developer?

AspectFintech ProgrammerFintech Software Developer
Required CredentialsBachelor's in Computer Science or related field; coding certificationsBachelor's in Computer Science or related field; coding certifications
Work EnvironmentFinancial tech companies, banks, startupsFinancial tech companies, banks, startups
Employer & Industry UsageUsed interchangeably in industry; focuses on coding and implementationOften used interchangeably; emphasizes software development lifecycle

Both roles involve coding and working within financial technology environments, with overlapping credentials and work settings. The main difference lies in terminology preference; 'Fintech Programmer' often emphasizes coding tasks, while 'Fintech Software Developer' may encompass broader software development responsibilities.

What are popular job titles related to Fintech Programmer jobs in Pennsylvania?

For Fintech Programmer jobs in Pennsylvania, the most frequently searched job titles are:

What job categories do people searching Fintech Programmer jobs in Pennsylvania look for?

The top searched job categories for Fintech Programmer jobs in Pennsylvania are:

Fintech Programmer jobs near you
Infographic showing various Fintech Programmer job openings in Pennsylvania as of June 2026, with employment types broken down into 91% Full Time, and 9% Contract. Highlights an 54% In-person, 18% Hybrid, and 28% Remote job distribution.

Principal Data Engineer

Medical Guardian

Philadelphia, PA • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 17 days ago

Job description

About Medical Guardian:
Medical Guardian is a fast-growing digital health and safety company on a mission to help people live a life without limits. With 13 consecutive years on the Inc. 5000 list of Fastest Growing Companies, we are redefining what it means to age confidently and independently.
We support over 625,000 members nationwide with life-saving emergency response systems and remote patient monitoring solutions. Trusted by families, healthcare providers, and care managers, our work is powered by a culture of innovation, compassion, and purpose.
Mission:
This role is focused on building and leading the data engineering foundation that powers real-time decisioning, operational applications, analytics, ML/AI model development, and data services across Medical Guardian.
The Principal Data Engineer will own the design, delivery, and maturity of production-grade data pipelines and data platforms, with a primary emphasis on real-time streaming, IoT telemetry, Databricks, Azure, data services for APIs and microservices, and reliable data products for downstream consumption.
Role Summary:
We are looking for a Principal Data Engineer to serve as a hands-on technical and people leader for data engineering, data platform architecture, real-time streaming, and production data services. This role will focus on designing, building, operating, and improving data pipelines and data products while also bringing principal-level judgment to architecture, stakeholder shaping, delivery priorities, team management, and production readiness.
This is a hands-on engineering leadership role first. The ideal candidate should be comfortable spending significant time working directly with Databricks, Spark, SQL, Python/PySpark, Azure services, streaming architectures, data quality frameworks, pipeline automation, CI/CD, and production troubleshooting. They should also be able to operate with the maturity of a principal-level leader: shaping unclear requirements, making pragmatic technical decisions, managing and mentoring engineers, and driving work forward without waiting for perfect specifications.
This is a fast-moving, startup-like environment. Requirements may be incomplete, priorities may evolve, and the right candidate will help create clarity while building quickly. We need someone who can move from ambiguous business need to reliable data capability with urgency, discipline, and ownership.
Stakeholder shaping is a critical part of this role. The Principal Data Engineer should be able to work directly with business, product, software engineering, analytics, ML/AI, operations, and leadership stakeholders to define what data needs to exist, how it should be consumed, what production guarantees are required, and how success should be measured.
A background in commercial software, SaaS, digital products, healthtech, fintech, IoT, data platforms, or other product-driven environments is strongly preferred. We want someone who understands that data pipelines and data services are not just technical artifacts. They are product capabilities that support real users, real workflows, operational decisions, ML/AI systems, APIs, analytics, and measurable business outcomes.
Key Responsibilities:
Hands-On Data Engineering and Platform Development
  • Design, build, optimize, and operate production-grade batch and streaming data pipelines on Azure and Databricks, with a primary focus on real-time IoT and telemetry use cases within a Medallion architecture.
  • Develop ETL/ELT workflows to ingest, transform, validate, and serve large volumes of structured, semi-structured, unstructured, and streaming data.
  • Build and maintain reliable data products, data services, APIs, and microservices that support operational applications, analytics, software engineering, and ML/AI teams.
  • Use Python, PySpark, Spark SQL, SQL, Delta Lake, Databricks Workflows, CI/CD, and related tools to build maintainable, testable, and observable data systems.
  • Troubleshoot complex production pipeline issues across Databricks, Azure, streaming systems, APIs, and source systems, including root cause analysis, corrective action, and prevention planning.
  • Move quickly from rough business need to prototype, pilot, and production-ready data capability while maintaining appropriate engineering discipline.

Real-Time Streaming, IoT Telemetry, and Operational Data Services
  • Lead the design and delivery of real-time streaming ingestion and processing patterns for connected medical device telemetry, event data, and operational data feeds.
  • Implement streaming solutions using Azure Event Hubs, Azure Stream Analytics, Databricks, Delta Lake, and related Azure integration patterns.
  • Design cost-effective throughput, partitioning, delivery, retention, and replay strategies for high-volume event and telemetry workloads.
  • Create consumption patterns that support APIs, microservices, operational applications, near-real-time decisioning, analytics, and ML/AI use cases.
  • Define reliability, latency, quality, observability, and supportability expectations for production streaming systems.

Databricks, Lakehouse, and Data Platform Architecture
  • Set direction for Databricks-based data engineering patterns, including Medallion architecture, Delta Lake, Spark optimization, data modeling, data quality, and reusable pipeline design.
  • Optimize production Databricks pipelines using PySpark, Spark SQL, Delta Lake, partitioning strategies, caching, shuffle optimization, cluster/job configuration, and cost-aware design.
  • Establish practical standards for pipeline structure, code organization, testing, deployment, monitoring, documentation, and ownership.
  • Partner with data platform, security, infrastructure, and engineering teams to ensure the data platform is scalable, secure, reliable, and aligned with enterprise architecture.
  • Make pragmatic architecture tradeoffs between speed, durability, cost, governance, performance, and downstream business impact.

Stakeholder Shaping and Cross-Functional Partnership
  • Work directly with business, product, analytics, ML/AI, operations, software engineering, and leadership stakeholders to clarify what data is needed, why it matters, how it will be used, and what success looks like.
  • Translate ambiguous business needs into concrete data requirements, data product definitions, architecture options, delivery priorities, and implementation plans.
  • Ask practical questions early: who will use the data, what decision or workflow does it support, what latency and quality are required, what happens if the data is wrong or late, and how will we know the capability is creating value?
  • Help the organization avoid becoming a data ticket factory by shaping solutions, not just executing requests.
  • Communicate architecture decisions, tradeoffs, risks, dependencies, and delivery options clearly to technical and non-technical stakeholders.

Team Management and Principal-Level Technical Leadership
  • Manage, mentor, and develop data engineers, providing clear expectations, technical guidance, prioritization support, feedback, and accountability.
  • Provide technical leadership through hands-on example, strong engineering judgment, clear recommendations, and pragmatic decision-making.
  • Lead design reviews, code reviews, production readiness reviews, incident reviews, and architecture discussions across data engineering initiatives.
  • Establish and improve engineering standards for data quality, testing, CI/CD, observability, documentation, runbooks, cost management, privacy, and security.
  • Proactively identify platform risks, data gaps, unclear ownership, operational weaknesses, and opportunities to improve reliability, scalability, and delivery speed.
  • Influence without relying only on formal authority by building trust, framing tradeoffs, and helping cross-functional teams get to decisions.

ML/AI, Analytics, and GenAI Enablement
  • Partner with ML engineers, data scientists, analytics engineers, and analysts to deliver reliable data pipelines, feature pipelines, training datasets, scoring inputs, and feedback loops.
  • Support the data foundation for predictive models, risk scores, operational decisioning, GenAI workflows, RAG, document intelligence, summarization, and AI-enabled automation.
  • Help define data contracts, model-ready datasets, feature definitions, lineage, and monitoring expectations for ML/AI and analytics use cases.
  • Ensure downstream consumers understand the meaning, freshness, quality, limitations, and appropriate use of the data products they depend on.

Governance, Data Quality, Security, and Production Operations
  • Apply privacy-first, security-aware, and governance-aligned practices for regulated, sensitive, and operationally critical data.
  • Design and implement data quality checks, validation rules, anomaly detection, schema expectations, alerting, and operational monitoring.
  • Ensure production pipelines and services are supportable, observable, documented, recoverable, and aligned with business continuity needs.
  • Drive incident response and continuous improvement for data platform and pipeline issues, including root cause analysis and preventative remediation.
  • Balance innovation with reliability, compliance, privacy, cost discipline, and operational usefulness.

Required Qualifications:
  • 10+ years of professional experience in data engineering, software engineering, data platform engineering, distributed systems, analytics engineering, or related technical fields.
  • 7+ years of hands-on experience designing, building, optimizing, and operating production data pipelines, data platforms, or data services.
  • 5+ years of hands-on experience with modern cloud data platforms, including Databricks, Spark, Delta Lake, SQL, Python/PySpark, and production pipeline orchestration.
  • 3+ years of experience leading, managing, mentoring, or providing technical direction to data engineers or related technical teams.
  • Strong experience with Azure cloud services for data engineering, streaming, integration, storage, security, and production operations.
  • Experience designing and operating real-time streaming, event-driven, or near-real-time data pipelines in production or business-critical environments.
  • Experience applying DevOps, CI/CD, testing, version control, code review, documentation, and automation practices to data engineering workloads.
  • Experience building data services, APIs, microservices, or reusable consumption patterns for downstream applications, analytics, ML/AI, or operational workflows.
  • Strong understanding of data quality, observability, monitoring, lineage, reliability, cost optimization, privacy, and production support for data systems.
  • Experience translating ambiguous business needs into technical designs, architecture recommendations, delivery plans, and measurable outcomes.
  • Ability to explain data architecture, pipeline behavior, tradeoffs, assumptions, risks, and limitations to both technical and non-technical stakeholders.
  • Strong ownership mindset and ability to drive work forward independently in a fast-moving, evolving environment.

Preferred Qualifications:
  • 12+ years of relevant professional experience in data engineering, software engineering, data platforms, distributed systems, analytics engineering, commercial software, or production data products.
  • Experience working as a principal, staff, lead, manager, or architect-level data engineering leader in a production environment.
  • Experience managing direct reports, setting team priorities, developing engineers, and improving team execution and accountability.
  • Experience working in commercial software, SaaS, digital products, healthtech, fintech, IoT, consumer technology, or other product-driven environments.
  • Experience in startup, scale-up, innovation, new product development, or rapid-build environments where the candidate had to operate with ambiguity and drive work forward independently.
  • Experience with Azure Event Hubs, Azure Stream Analytics, Azure Service Bus, Azure Data Factory, Azure Functions, ADLS, Azure Cosmos DB, Event Grid, or similar Azure services.
  • Experience with medical device telemetry, IoT data, remote patient monitoring, healthcare operations, regulated data, HIPAA-sensitive environments, or safety-critical workflows.
  • Experience building data platforms or data products that support APIs, microservices, operational applications, ML/AI systems, GenAI workflows, RAG, analytics, and executive reporting.
  • Experience with data contracts, semantic layers, feature stores, model-ready datasets, data lineage, schema evolution, CDC, and operational feedback loops.
  • Experience with performance tuning, cost optimization, FinOps practices, data platform reliability, and production incident management.
  • Experience partnering with product managers, software engineers, ML engineers, analysts, business leaders, and operations teams to turn data into usable business capabilities.
  • Experience building MVPs, validating assumptions, iterating based on feedback, and maturing prototypes into durable production systems.

Requirements
Benefits
  • Health Care Plan (Medical, Dental & Vision)
  • Paid Time Off (Vacation, Sick Time Off & Holidays)
  • Company Paid Short Term Disability and Life Insurance
  • Retirement Plan (401k) with Company Match

Apply