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Remote Data Integration Developer Jobs in Marlton, NJ

IntegriChain

Sr Data Engineer

Philadelphia, PA · On-site +1

$115K - $138K/yr

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 ...

Lincoln Financial

Associate ETL Developer

Radnor, PA · On-site +1

$55K - $100K/yr

... AWS Glue to integrate data across Oracle and PostgreSQL systems. You will collaborate with ... best practices in data engineering, and apply them when appropriate • Support process ...

Ennoble Care

Director of Data Engineering

Marlton, NJ · Remote

$116K - $139K/yr

Ennoble Care offers a variety of programs including, remote patient monitoring, behavioral health ... small team * DevOps: Experience integrating pipelines with CI/CD, version control, branching ...

Ennoble Care

Director of Data Engineering

Philadelphia, PA · Remote

$115K - $138K/yr

Ennoble Care offers a variety of programs including, remote patient monitoring, behavioral health ... small team * DevOps: Experience integrating pipelines with CI/CD, version control, branching ...

Tuesday Health

Software Developer

Philadelphia, PA · Remote

Identity, security, and data * Integrate authentication/authorization via Microsoft Entra ID (OIDC ... Remote work with multiple onsite sessions each year to maximize collaboration and team building * A ...

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How much do remote data integration developer jobs pay per hour?

As of Aug 6, 2026, the average hourly pay for remote data integration developer in Marlton, NJ is $52.59, according to ZipRecruiter salary data. Most workers in this role earn between $44.28 and $59.18 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a remote data integration developer?

To thrive as a Remote Data Integration Developer, you need a strong background in database management, ETL processes, and programming languages like SQL, Python, or Java, often supported by a degree in computer science or related fields. Familiarity with data integration platforms (e.g., Informatica, Talend, MuleSoft), cloud services (AWS, Azure), and relevant certifications are typically required. Excellent problem-solving skills, attention to detail, and effective remote communication are crucial soft skills for collaborating across distributed teams. These skills and qualifications ensure the seamless, secure, and efficient transfer of data across systems, which is vital for organizational decision-making and operations.

What is the difference between Remote Data Integration Developer vs Data Engineer?

AspectRemote Data Integration DeveloperData Engineer
Required CredentialsTypically requires SQL, ETL tools, and data integration certificationsRequires SQL, programming languages (Python, Java), and sometimes cloud certifications
Work EnvironmentPrimarily remote, focused on data pipelines and integration tasksOften remote or on-site, involved in building and maintaining data infrastructure
Employer & Industry UsageUsed in tech, finance, healthcare for data flow managementCommon in tech, finance, and large enterprises for data architecture

The Remote Data Integration Developer focuses on designing and implementing data pipelines and integrating data from various sources, often working with ETL tools. Data Engineers build and maintain the overall data infrastructure, including data warehouses and pipelines, requiring broader programming skills. Both roles are essential in data management but differ in scope and technical depth.

What are some common challenges remote data integration developers face when collaborating with distributed teams?

Remote Data Integration Developers often work with teams spread across different time zones and departments, which can present challenges in communication and project coordination. Ensuring data consistency and managing integration schedules require clear documentation and regular virtual meetings to stay aligned. Additionally, troubleshooting integration issues remotely may involve navigating various system environments without direct on-site access. Proactive communication and the use of collaborative tools help overcome these obstacles and support successful project delivery.

What is a remote data integration developer?

A Remote Data Integration Developer is an IT professional who specializes in designing, building, and maintaining systems that combine data from different sources, working entirely from a remote location. They use tools and programming languages to create automated data pipelines, ensure data quality, and enable seamless data flow across platforms. Their role is crucial for organizations needing accurate, up-to-date data for analysis and decision-making. Remote Data Integration Developers often collaborate with data engineers, analysts, and stakeholders, using cloud-based tools to facilitate their work.
Remote Data Integration Developer jobs near you

Sr Data Engineer

IntegriChain

Philadelphia, PA • On-site, Remote

$115K - $138K/yr

Full-time

Medical, PTO

Re-posted 13 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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