We just got in this role: It is with Medical Guardian-this is a hybrid role onsite 3 days a week in Center City, Philadelphia.
This is a 4 month contract to hire with no conversion fee. Converting salary $160K annually. They can not sponsor so of US Citz.
Interview mode 1st round will be Virtual & Final will be onsite.
The Lead 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.
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 our Philadelphia-based customer.
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 Lead 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.
