Lead Data Platform Engineer

CentraState Healthcare System

Lead Data Platform Engineer handling the technical architecture for the Enterprise Data Analytics Platform team. Driving large-scale engineering initiatives across the organization while mentoring engineers.

Posted 6/5/2026full-timeRemote • New Jersey • 🇺🇸 United StatesSeniorWebsite

Tech Stack

Tools & technologies

Amazon RedshiftApacheBigQueryCloudDistributed SystemsJavaKafkaPythonScalaSparkSQL

About the role

Key responsibilities & impact

Own the technical architecture of the data platform end-to-end: ingestion, storage, transformation, orchestration, serving, and observability layers.
Author and maintain the platform architectural vision document; lead quarterly architecture reviews to assess alignment with organizational goals and technology trends.
Define and evolve the target-state architecture for the platform, establishing a multi-year technology roadmap in partnership with engineering leadership.
Evaluate emerging technologies, frameworks, and make evidence-based adoption recommendations.
Serve as the final technical escalation point for complex design questions, cross-team conflicts, and build-vs-buy decisions affecting the platform.
Lead the planning, execution, and delivery of multi-quarter platform initiatives involving multiple engineers, cross-functional dependencies, and significant organizational impact.
Break down large, ambiguous programs into scoped workstreams; assign technical leads per workstream, define milestones, and manage cross-team dependencies and risk.
Drive initiative kick-offs with clear problem framing, success criteria, architectural constraints, and delivery phasing — from 0-to-1 exploration through production hardening.
Maintain stakeholder alignment throughout delivery: proactively communicate status, surface trade-offs, and escalate blockers to leadership before they become risks.
Lead post-mortems and retrospectives for large initiatives; document and socialize lessons learned to raise the organizational bar on delivery excellence.
Exercise functional oversight across the EDAP team: review technical designs, set quality gates, approve architectural decisions, and ensure consistency of implementation patterns.
Define, document, and socialize platform engineering standards including coding conventions, testing requirements, CI/CD practices, schema design guidelines, and SLA frameworks.
Establish and own the platform's technical review process (design review): triage incoming projects, chair review sessions, and ensure decisions are documented and traceable.
Identify and drive resolution of technical debt, redundancy, and architectural drift that impede platform reliability, developer productivity, or scalability.
Partner with security, compliance, and infrastructure teams to ensure platform systems meet governance, data privacy, and regulatory requirements by design.
Serve as a primary technical mentor for junior and senior data platform engineers across the platform; provide structured coaching on system design, technical communication, and engineering judgment.
Conduct and lead design reviews, architecture critiques, and technical deep-dives that strengthen the overall capability of the engineering team.
Define what technical excellence looks like at each level of engineers and participate in calibration discussions.
Represent the data platform in engineering-wide forums, all-hands, and external venues (conferences, open-source communities, recruiting events).
Build a culture of documentation, reliability, and platform-as-a-product thinking across all data platform engineering functions.
Participate in goal planning cycles as a technical voice; define engineering-led goals that improve platform reliability, developer experience, and data quality.

Requirements

What you’ll need

8–14 years of professional software or data engineering experience, with at least 3 years in a technical lead, principal, capacity on a data platform or distributed systems team.
Demonstrated track record of leading large-scale, cross-functional data infrastructure projects from conception through production with measurable business impact.
Deep expertise in distributed data systems: data warehouse/lakehouse architecture, streaming platforms, large-scale batch processing, and cloud-native data infrastructure.
Expert-level proficiency in Python and SQL; strong working knowledge of at least one JVM language (Scala or Java) for Spark or Flink development.
Deep knowledge of modern data warehouse and lakehouse platforms (Snowflake, BigQuery, Redshift, Databricks, or equivalent) including storage optimization, compute management, and cost governance.
Strong command of open table formats (Apache Iceberg, Delta Lake, or Apache Hudi) and their trade-offs in production lakehouse architectures.
Experience architecting and operating large-scale streaming pipelines with Apache Kafka, Kinesis, or Pub/Sub, including schema management, consumer group design, and exactly-once semantics..
Exceptional communication skills: ability to write clearly, present confidently, and adapt technical depth to a wide range of audiences from individual contributors to high level leaders.
Experience establishing engineering standards, review processes, and quality gates across multi-team engineering organizations.

Benefits

Comp & perks

Medical, Dental, Vision, Prescription Coverage (22.5 hours per week or above for full-time and part-time team members)
Life & AD&D Insurance.
Short-Term and Long-Term Disability (with options to supplement)
403(b) Retirement Plan: Employer match, additional non-elective contribution
PTO & Paid Sick Leave
Tuition Assistance, Advancement & Academic Advising
Parental, Adoption, Surrogacy Leave
Backup and On-Site Childcare
Well-Being Rewards
Employee Assistance Program (EAP)
Fertility Benefits, Healthy Pregnancy Program
Flexible Spending & Commuter Accounts
Pet, Home & Auto, Identity Theft and Legal Insurance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonSQLScalaJavaApache KafkaKinesisPub/SubSnowflakeBigQueryRedshift

Soft Skills

communicationleadershipproblem framingstakeholder alignmentmentoringdocumentationcollaborationorganizational impacttechnical judgmentdelivery excellence