Lead Data Engineer

About The Role

We're looking for a seasoned Lead Data Engineer to own and drive our cloud-native

data platform development end-to-end. This is a high-impact, hands-on leadership role

where you'll architect scalable data and database systems, ship production-grade

pipelines, and guide a growing team — all while keeping a sharp eye on business

outcomes.

You'll tackle engineering challenges across distributed systems, large-scale databases,

and multi-cloud data infrastructure. If you thrive at the intersection of deep systems-level

work and cross-functional collaboration, this role is for you.

What You'll Do

• Architect & Build: Design, implement, and maintain scalable, production-grade
  
  

data platforms across multi-cloud, multi-tenant environments (AWS, Azure,

GCP). Build database and storage solutions that work seamlessly across cloud

providers and diverse deployment models.

• Scale Database Systems: Own the design and operation of database
  
  

infrastructure supporting a large number of tables, high-throughput operations,

and complex query workloads — scaling through 100x+ growth while maintaining

reliability and performance.

• Lead Delivery: Own project timelines, priorities, and stakeholder communication.
  
  

Drive data engineering initiatives from ideation through production with a bias for

outcomes over activity.

• Set Technical Direction: Define data architecture standards, tooling choices,
  
  

and engineering best practices. Make critical build vs. buy decisions for data and

database technologies.

• Mentor & Grow the Team: Provide technical mentorship, conduct code reviews,
  
  

and help shape a high-performing data engineering culture.

• Collaborate Cross-Functionally: Partner closely with product, analytics, ML/AI,
  
  

platform, and infrastructure teams to ensure data systems power real business

value.

• Operate with Ownership: Monitor data quality, pipeline reliability, and platform
  
  

health. Own what you build from design through decommission. Treat production

like a product.

What You Bring Required

• 6+ years of hands-on experience in cloud-native data engineering, spanning
  
  

ingestion, transformation, orchestration, storage, governance, and observability.

• Deep expertise in modern distributed systems — you understand consensus,
  
  

partitioning, replication, fault tolerance, and have built or operated distributed

data infrastructure at scale.

• Scalable database architecture — proven experience designing and managing
  
  

database systems with a large number of tables, high-volume OLTP/OLAP

workloads, and complex operational patterns. You've scaled databases through

massive growth at high-growth companies.

• 1+ years of project management experience — you've owned roadmaps,
  
  

managed delivery timelines, coordinated across teams, and are comfortable with

tools like Jira.

• Deep expertise in scalable, multi-cloud, multi-tenant data architecture — you
  
  

understand the trade-offs and have built systems that serve diverse workloads

across GCP, AWS, Azure, first-party and third-party deployment models.

• Strong proficiency in modern data stack technologies such as Spark, Kafka,
  
  

Airflow/Dagster, dbt, Snowflake, Databricks, Delta Lake/Iceberg, or

equivalent.

• Deep experience with distributed database systems — PostgreSQL, MySQL,
  
  

DynamoDB, or similar — including performance tuning, schema design at scale,

and operational reliability.

• Proficiency in Python, SQL, and Java/Scala, and at least one infrastructure-as-
  
  

code framework (Terraform, Pulumi, etc.).

• Experience with data quality, data profiling, data integration, and data
  
  

governance — you can engineer solutions that ensure secure and consistent

data consumption across platforms.

• A production-first, outcome-oriented mindset — you measure success by
  
  

what's running reliably in production, not by what's in a slide deck. Customer

value over story-point velocity.

• Excellent communication skills — you can translate complex technical concepts
  
  

for both engineering peers and business stakeholders.

Preferred

• 1+ years of tech/data team management experience — you've directly
  
  

managed engineers, run standups, handled performance conversations, and built

team culture.

• Experience with the AI-native stack — vector databases (Pinecone, Weaviate,
  
  

pgvector), RAG pipelines, feature stores, LLM orchestration frameworks

(LangChain, LlamaIndex), and ML pipeline tooling (MLflow, Kubeflow,

SageMaker).

• Background in the healthcare / life sciences domain — familiarity with HL7/
  
  

FHIR, HIPAA/GxP compliance, EHR/EMR data, clinical data models, claims/

patient data, health data interoperability standards, or experience processing

large volumes of commercial and medical data.

• Experience with Redis, Temporal, async job processing frameworks, or other
  
  

infrastructure supporting high-throughput distributed workloads.

• Experience with real-time/streaming architectures (Kafka Streams, Flink,
  
  

Spark Structured Streaming).

• Track record of building multi-cloud or hybrid cloud database solutions.
• Experience with database orchestration and automation at scale.
• Familiarity with data mesh or data product paradigms.
• Strong testing discipline — experience creating comprehensive automated unit
  
  

and integration tests for data systems.