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Remote Senior Data Engineer Jobs in Philadelphia, PA

IntegriChain

Sr Data Engineer

Philadelphia, PA · On-site +1

$115K - $138K/yr

Comfortable operating as a hands-on senior individual contributor who can also influence strategy and engineering standards. Preferred Experience * Experience with Reltio MDM, including inbound data ...

Pepperidge Farm, Incorporated

Data Engineer

Camden, NJ · On-site +1

$110K - $152K/yr

In this role, you will work closely with senior data engineers, solution architects, and business ... remote for the right candidate. Compensation and Benefits: The target base salary range for this ...

Campbell's

Data Engineer

Camden, NJ · On-site +1

$110K - $152K/yr

In this role, you will work closely with senior data engineers, solution architects, and business ... remote for the right candidate. Compensation and Benefits: The target base salary range for this ...

SITA Switzerland Sarl

Senior Data Engineer

Cedar Brook, NJ · On-site +1

$104K - $142K/yr

You'llsupport portfolio teams by enabling governance-aligned data engineering practices, ensuring data is produced in a consistent, secure, and scalable way without owning team data products or ...

QVC

Data Engineer

West Chester, PA · Remote

$108K - $130K/yr

Your Opportunity, Your Team The Data Engineer supports QVC and will collaborate with an Agile team ... Where You'll Work This role is remote; job seekers must reside in one of the following states to be ...

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All JobsData Engineer JobsRemote Senior Data Engineer JobsRemote Senior Data Engineer Jobs in Philadelphia, PA

Remote Senior Data Engineer information

See Philadelphia, PA salary details

$81.7K

$127.5K

$176.6K

How much do remote senior data engineer jobs pay per year?

As of Aug 15, 2026, the average yearly pay for remote senior data engineer in Philadelphia, PA is $127,476.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,000.00 and $145,300.00 per year, depending on experience, location, and employer.

What is the difference between Remote Senior Data Engineer vs Data Scientist?

AspectRemote Senior Data EngineerData Scientist
Required CredentialsBachelor's/Master's in CS, Data Engineering certificationsBachelor's/Master's in CS, Data Science or related fields, certifications
Work EnvironmentData pipelines, ETL processes, cloud platformsData analysis, modeling, visualization tools
Industry UsageTech, finance, healthcare, e-commerceTech, marketing, research, finance

Remote Senior Data Engineers focus on building and maintaining data infrastructure, while Data Scientists analyze data to generate insights. Both roles often collaborate but serve different functions within data teams. Understanding these differences helps in choosing the right career path or job search focus.

What is a remote senior data engineer?

Remote Senior Data Engineers are experienced professionals who design, build, and maintain complex data systems and pipelines, but work from a location outside of the company's main office. They are responsible for ensuring reliable data flow, optimizing data storage, and supporting analytics, often collaborating with teams across different time zones. Their role typically involves advanced programming, data architecture, and the implementation of best practices in data engineering, all while working remotely. This position demands strong technical skills, excellent communication, and the ability to work independently.

How does a remote senior data engineer typically collaborate with distributed teams to ensure data pipeline reliability?

As a Remote Senior Data Engineer, collaboration with cross-functional, distributed teams is usually facilitated through agile project management tools, regular video meetings, and shared documentation platforms. You’ll often work closely with data scientists, analysts, and DevOps engineers to design, build, and maintain scalable data pipelines. Clear communication and proactive status updates are essential to ensure everyone stays aligned and issues are addressed quickly. Leveraging version control systems and automated testing also helps maintain the reliability and quality of the data infrastructure across different time zones.

What are the key skills and qualifications needed to thrive as a remote senior data engineer?

To thrive as a Remote Senior Data Engineer, you need advanced expertise in data engineering concepts, SQL, and programming languages like Python or Scala, along with a degree in computer science or a related field. Familiarity with cloud platforms (such as AWS, Azure, or GCP), big data frameworks (like Spark or Hadoop), and relevant certifications are typically required. Excellent problem-solving, communication, and self-management skills are crucial for collaborating remotely and driving projects independently. These competencies ensure robust data pipelines, effective teamwork, and successful delivery of scalable data solutions in distributed environments.

What are the most commonly searched types of Senior Data Engineer jobs in Philadelphia, PA?

The most popular types of Senior Data Engineer jobs in Philadelphia, PA are:

What are popular job titles related to Remote Senior Data Engineer jobs in Philadelphia, PA?

For Remote Senior Data Engineer jobs in Philadelphia, PA, the most frequently searched job titles are:

What job categories do people searching Remote Senior Data Engineer jobs in Philadelphia, PA look for?

The top searched job categories for Remote Senior Data Engineer jobs in Philadelphia, PA are:

What cities near Philadelphia, PA are hiring for Remote Senior Data Engineer jobs?

Cities near Philadelphia, PA with the most Remote Senior Data Engineer job openings:

Remote Senior Data Engineer jobs near you
Infographic showing various Remote Senior Data Engineer job openings in Philadelphia, PA as of August 2026, with employment types broken down into 90% Full Time, and 10% Contract. Highlights an 100% Remote job distribution, with an average salary of $127,476 per year, or $61.3 per hour.

Sr Data Engineer

IntegriChain

Philadelphia, PA • On-site, Remote

$115K - $138K/yr

Full-time

Medical, PTO

Re-posted 22 days ago

Job description

Company Description
IntegriChain is the data and application backbone for market access departments of Life Sciences manufacturers. We deliver the data, the applications, and the business process infrastructure for patient access and therapy commercialization. More than 250 manufacturers rely on our ICyte Platform to orchestrate their commercial and government payer contracting, patient services, and distribution channels. ICyte is the first and only platform that unites the financial, operational, and commercial data sets required to support therapy access in the era of specialty and precision medicine. With ICyte, Life Sciences innovators can digitalize their market access operations, freeing up resources to focus on more data-driven decision support. With ICyte, Life Sciences innovators are digitalizing labor-intensive processes - freeing up their best talent to identify and resolve coverage and availability hurdles and to manage pricing and forecasting complexity.
We are headquartered in Philadelphia, PA (USA), with offices in: Ambler, PA (USA); Pune, India; and Medellín, Colombia. For more information, visit www.integrichain.com, or follow us on Twitter @IntegriChain and LinkedIn.
Job Description
Position Overview
  • Enterprise data leadership: Help define and mature data integration, data consolidation, MDM integration, and data platform design patterns across Integrichain.
  • Hands-on Snowflake engineering: Design, build, optimize, and operate Snowflake data models, pipelines, stored procedures, and high-volume data processing patterns.
  • MDM/Reltio enablement: Partner with MDM and Product teams to support HCO Master data ingestion, outbound extracts, cross-reference data, golden record consumption, survivorship outputs, and downstream publishing patterns.
  • Cross-functional partnership: Work with Product, Engineering, MDM, Data Science, DevOps, Security, and business stakeholders to align data solutions to enterprise priorities.
  • Modern ELT execution: Use dbt or similar ELT tooling to develop reliable, maintainable, testable, and observable data pipelines.
  • Cost and performance ownership: Drive Snowflake performance tuning, warehouse sizing, workload management, cost tracking, and cost optimization practices.

Key Responsibilities
Data Strategy, Consolidation, and Integration
  • Partner with Data Science leadership to rationalize and consolidate the enterprise data landscape across products, platforms, and acquired capabilities.
  • Define reusable data integration patterns for batch, micro-batch, near-real-time, and application-to-application data exchange.
  • Collaborate with cross-functional teams to understand business data needs, source-system realities, and enterprise application integration requirements.
  • Design scalable patterns for ingesting, transforming, mastering, and publishing data across operational and analytical use cases.
  • Help establish standards for data contracts, schema evolution, data quality, lineage, and data ownership.

MDM / Reltio Data Engineering Enablement
  • Design and build data pipelines that load source data into Reltio MDM and extract mastered outputs from Reltio for downstream Snowflake, analytics, AI, and operational use cases.
  • Partner with MDM configuration and Product Management teams to translate HCO mastering requirements into data pipeline, mapping, validation, reconciliation, and publishing patterns.
  • Work with Reltio APIs, exports, crosswalks/XREFs, event-based integration patterns, and bulk load/extract mechanisms as needed to support inbound and outbound data flows.
  • Engineer integration patterns for HCO Master data, including party/entity, address, identifier, hierarchy, relationship, match/merge, survivorship, and golden record outputs.
  • Support source ingestion and reference data integration involving datasets such as HIN, DEA, NPI, NCPDP, 340B/PHS, channel outlet data, customer/account data, and other life sciences master/reference sources.
  • Develop validation and reconciliation processes to compare source data, Reltio mastered data, Snowflake curated data, and downstream consumption layers.
  • Help operationalize MDM outputs for business-facing data products, semantic models, reporting tables, APIs, and AI-ready datasets.

Snowflake Platform Engineering and Optimization
  • Design Snowflake database, schema, table, view, and semantic-layer patterns that support performance, governance, and maintainability.
  • Optimize Snowflake workloads using clustering, micro-partition awareness, warehouse sizing, query profiling, caching behavior, and workload isolation.
  • Implement Snowflake cost tracking and optimization practices, including warehouse utilization monitoring, inefficient query identification, and cost allocation by workload, team, or use case.
  • Build scalable SQL and Snowflake stored procedure logic for large-volume data processing and analytical workloads.
  • Apply secure Snowflake design patterns including RBAC, masking, access isolation, auditing, and environment separation.

ETL/ELT, dbt, Python, and Data Pipeline Development
  • Design, build, and maintain reliable ELT pipelines using dbt or comparable modern data transformation tooling.
  • Develop Python-based automation for API integration, file processing, metadata management, validation, orchestration support, and operational tooling.
  • Develop modular, tested, and reusable transformation models for raw, curated, mastered, and business-ready data layers.
  • Implement automated data quality checks, source freshness checks, reconciliation, logging, and exception-handling patterns.
  • Build orchestration-ready pipelines that support dependency management, restartability, incremental loads, and operational monitoring.
  • Collaborate with DevOps/SRE teams on CI/CD, deployment automation, environment promotion, and operational runbooks for data pipelines.

Data Modeling and Big Data Processing
  • Spearhead logical and physical data modeling efforts for enterprise analytical, operational, MDM, and AI-ready datasets.
  • Design models that balance normalization, dimensional modeling, medallion/lakehouse concepts, and application-specific consumption needs.
  • Create denormalized reporting and semantic-model-ready structures that simplify business consumption and reduce ambiguity for AI/LLM use cases.
  • Process and optimize large data volumes in Snowflake using efficient SQL, PL/SQL-style procedural logic, Snowflake Scripting, and performance-aware design.
  • Create reusable patterns for historical tracking, snapshots, audit columns, data versioning, and lifecycle management.
  • Ensure data models support downstream BI, AI/ML, semantic models, data apps, MDM Explorer/Entity 360 use cases, and enterprise reporting.

Qualifications
Required Skills and Experience
  • 10+ years of experience in data engineering, database engineering, analytics engineering, or data platform development in production environments.
  • Strong hands-on experience with Snowflake, including architecture, performance tuning, security design, cost optimization, and cost tracking.
  • Thorough understanding of Snowflake design patterns for analytical workloads, high-volume data processing, data sharing, and multi-environment deployments.
  • Hands-on experience with ETL/ELT tools; dbt experience is strongly preferred.
  • Strong SQL and PL/SQL-style development experience, including complex transformations, stored procedures, performance tuning, and large-scale data processing.
  • Python experience for data automation, API integration, file handling, data validation, metadata processing, or operational tooling.
  • Experience designing and implementing enterprise data models, curated data layers, semantic layers, and reusable data products.
  • Experience with data integration patterns across enterprise applications, APIs, files, cloud storage, operational systems, MDM platforms, and analytical platforms.
  • Working understanding of Master Data Management concepts such as golden records, crosswalks/XREFs, match/merge, survivorship, hierarchies, entity relationships, stewardship, and data quality.
  • Experience partnering with MDM, Product, or business teams to translate mastering requirements into source-to-target mappings, transformation logic, validations, and downstream data consumption patterns.
  • Ability to work directly with cross-functional stakeholders to gather requirements, explain design tradeoffs, and drive alignment.
  • Experience implementing data quality, lineage, auditability, observability, and operational monitoring within data pipelines.
  • Comfortable operating as a hands-on senior individual contributor who can also influence strategy and engineering standards.

Preferred Experience
  • Experience with Reltio MDM, including inbound data loads, outbound exports, Reltio APIs, crosswalks, match/merge outputs, survivorship outputs, and operational troubleshooting.
  • Experience in life sciences, healthcare, pharma commercialization, HCO/HCP mastering, patient data, channel data, customer master, or commercial data platforms.
  • Experience with life sciences reference and commercial datasets such as HIN, DEA, NPI, NCPDP, 340B/PHS, 844, 852, 867, chargebacks, gross-to-net, government pricing, PBR, or UBR.
  • Experience with orchestration frameworks such as Airflow, Dagster, dbt Cloud jobs, cloud-native schedulers, or similar tools.
  • Experience with cloud platforms and storage patterns, especially Azure or AWS object storage integrated with Snowflake.
  • Exposure to AI-ready data architecture, feature stores, ML datasets, semantic models, or AI/ML pipeline enablement.
  • Experience with Terraform, CI/CD, Git-based development, and infrastructure-as-code practices.
  • Snowflake SnowPro, Reltio, dbt, or equivalent cloud/data engineering certifications.

Additional Information
What does IntegriChain have to offer?
  • Mission driven: Work with the purpose of helping to improve patients' lives!
  • Excellent and affordable medical benefits + non-medical perks including Flexible Paid Time Off and much more!
  • Robust Learning & Development opportunities including over 700+ development courses free to all employees

#LI-KL1
IntegriChain is committed to equal treatment and opportunity in all aspects of recruitment, selection, and employment without regard to race, color, religion, national origin, ethnicity, age, sex, marital status, physical or mental disability, gender identity, sexual orientation, veteran or military status, or any other category protected under the law. IntegriChain is an equal opportunity employer; committed to creating a community of inclusion, and an environment free from discrimination, harassment, and retaliation.
Our policy on visa sponsorship for US based positions: Applicants for employment in the US must have valid work authorization that does not now and/or will not in the future require sponsorship of a visa for employment authorization in the US by IntegriChain.
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