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Contract Databricks Data Engineer Jobs in Pennsylvania

Armstrong World Industries

Data Engineer Job

Lancaster, PA · On-site +1

$125K - $145K/yr

Armstrong is seeking a Data Engineer to design, build, and maintain scalable data pipelines and ... Develop robust ETL/ELT workflows using modern tools (e.g., dbt, Airflow, Databricks, Glue). * Data ...

MHK TECH INC

Data Engineer

Malvern, PA · On-site

$112K - $134K/yr

MHK TECH INC is seeking a Data Engineer to design and maintain robust data pipelines and manage ... Databricks. • Develop ETL/ELT processes to ingest, transform, and store structured and ...

Armstrong World Industries

Data Engineer Job

Lancaster, PA · On-site +1

$125K - $145K/yr

Armstrong is seeking a Data Engineer to design, build, and maintain scalable data pipelines and ... Develop robust ETL/ELT workflows using modern tools (e.g., dbt, Airflow, Databricks, Glue). * Data ...

Chubb

AVP, Lead Data Engineer

Philadelphia, PA · On-site

$109K - $131K/yr

By joining Chubb as Lead Data Engineer for our North America Finance & Actuarial data platform, you ... Strong hands-on proficiency in Databricks (Delta Lake, Spark, notebooks, workflows) and Snowflake ...

Chubb

AVP, Lead Data Engineer

Philadelphia, PA

$115K - $138K/yr

By joining Chubb as Lead Data Engineer for our North America Finance & Actuarial data platform, you ... Strong hands-on proficiency in Databricks (Delta Lake, Spark, notebooks, workflows) and Snowflake ...

Archgroup

Manager, Data Engineer (Remote)

Home, PA · Remote

$100K - $174K/yr

We unify internal and external data on modern cloud platforms-including Snowflake and Databricks ... You will also guide engineers and reinforce strong delivery practices, while advancing the team ...

Archgroup

Manager, Data Engineer (Remote)

Home, PA · Remote

$100K - $174K/yr

We unify internal and external data on modern cloud platforms-including Snowflake and Databricks ... You will also guide engineers and reinforce strong delivery practices, while advancing the team ...

System One

Data Engineer - onsite

Hanover, PA · On-site

$110K - $132K/yr

Contract Compensation: 55-70/hr W-2, depending on experience Work Model: Onsite US Citizenship ... Hands-on experience with Azure Synapse, Azure Data Factory, Azure Databricks, and Azure Stream ...

System One

Data Engineer - onsite

Hanover, PA · On-site

$110K - $132K/yr

Contract Compensation: 55-70/hr W-2, depending on experience Work Model: Onsite US Citizenship ... Hands-on experience with Azure Synapse, Azure Data Factory, Azure Databricks, and Azure Stream ...

System One

Data Engineer - onsite

Hanover, PA · On-site

$110K - $132K/yr

Contract Compensation: 55-70/hr W-2, depending on experience Work Model: Onsite US Citizenship ... Hands-on experience with Azure Synapse, Azure Data Factory, Azure Databricks, and Azure Stream ...

System One

Data Engineer - onsite

Hanover, PA · On-site

$110K - $132K/yr

Contract Compensation: 55-70/hr W-2, depending on experience Work Model: Onsite US Citizenship ... Hands-on experience with Azure Synapse, Azure Data Factory, Azure Databricks, and Azure Stream ...

System One

Data Engineer - onsite

Hanover, PA · On-site

$110K - $132K/yr

Contract Compensation: 55-70/hr W-2, depending on experience Work Model: Onsite US Citizenship ... Hands-on experience with Azure Synapse, Azure Data Factory, Azure Databricks, and Azure Stream ...

System One

Data Engineer - onsite

Hanover, PA · On-site

$110K - $132K/yr

Contract Compensation: 55-70/hr W-2, depending on experience Work Model: Onsite US Citizenship ... Hands-on experience with Azure Synapse, Azure Data Factory, Azure Databricks, and Azure Stream ...

System One

Data Engineer - onsite

Hanover, PA · On-site

$110K - $132K/yr

Contract Compensation: 55-70/hr W-2, depending on experience Work Model: Onsite US Citizenship ... Hands-on experience with Azure Synapse, Azure Data Factory, Azure Databricks, and Azure Stream ...

System One

Data Engineer - onsite

Hanover, PA · On-site

$110K - $132K/yr

Contract Compensation: 55-70/hr W-2, depending on experience Work Model: Onsite US Citizenship ... Hands-on experience with Azure Synapse, Azure Data Factory, Azure Databricks, and Azure Stream ...

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All JobsContract Databricks Data Engineer JobsContract Databricks Data Engineer Jobs in Pennsylvania

Contract Databricks Data Engineer information

What is a contract Databricks data engineer?

Contract Databricks Data Engineers are professionals hired on a temporary or project basis to design, build, and maintain data infrastructure using Databricks, a unified analytics platform. They typically work with big data tools, cloud environments, and programming languages like Python or Scala to process and analyze large datasets. Their responsibilities often include developing data pipelines, optimizing data workflows, and collaborating with data scientists and analysts to support business objectives. Because they are contractors, their roles can vary by project and organization, offering flexibility and specialized expertise.

What skills and qualifications are needed to thrive as a contract Databricks data engineer?

To excel as a Contract Databricks Data Engineer, you need strong experience in data engineering, SQL, Spark, and cloud platforms, often supported by a degree in computer science or a related field. Familiarity with Databricks, Apache Spark, Python or Scala, and cloud services like AWS or Azure is typically required, along with relevant certifications. Excellent problem-solving, communication, and collaboration skills help you work effectively in dynamic, project-based environments. These competencies ensure the efficient design and implementation of scalable data solutions, driving business insights and project success.

What are common challenges faced by contract Databricks data engineers when integrating data from multiple sources?

As a contract Databricks Data Engineer, you'll often encounter challenges related to integrating diverse data sources, such as on-premises databases, cloud storage, and APIs. These challenges may include handling inconsistent data formats, managing data quality, and ensuring secure data transfers. Additionally, adapting to clients' unique data architectures and optimizing ETL pipelines for performance in a cloud environment are common tasks. Collaboration with data scientists, analysts, and other engineers is critical to ensure data is both accessible and reliable for downstream analytics and machine learning.

How much does a Contract Databricks Data Engineer make?

A Contract Databricks Data Engineer typically earns between $70 and $150 per hour, depending on experience, location, and project scope. Contract roles often pay higher hourly rates compared to full-time positions and may require expertise in Spark, cloud platforms, and data pipeline development.

Is a Contract Databricks Data Engineer in demand?

Contract Databricks Data Engineers are in high demand due to the increasing adoption of cloud-based data platforms and the need for expertise in big data processing, Spark, and SQL. Organizations seek professionals with skills in data pipeline development, cloud environments, and tools like Apache Spark and Delta Lake, often offering competitive rates for contract roles. The demand is driven by ongoing digital transformation initiatives across industries.

What are the most commonly searched types of Databricks Data Engineer jobs in Pennsylvania?

The most popular types of Databricks Data Engineer jobs in Pennsylvania are:

What are popular job titles related to Contract Databricks Data Engineer jobs in Pennsylvania?

For Contract Databricks Data Engineer jobs in Pennsylvania, the most frequently searched job titles are:

What job categories do people searching Contract Databricks Data Engineer jobs in Pennsylvania look for?

The top searched job categories for Contract Databricks Data Engineer jobs in Pennsylvania are:

What cities in Pennsylvania are hiring for Contract Databricks Data Engineer jobs?

Cities in Pennsylvania with the most Contract Databricks Data Engineer job openings:

Contract Databricks Data Engineer jobs near you

Principal Data Engineer

Medical Guardian

Philadelphia, PA • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 3 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