Lead Data Architect

Karsun Solutions
yesterday

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior
Compensation
$ 190K

Job location

Tech stack

API
Artificial Intelligence
Airflow
Amazon Web Services (AWS)
Cloud Computing
Code Review
Computer Engineering
Continuous Integration
Data Architecture
Information Engineering
Data Governance
ETL
Database Queries
Software Design Patterns
Github
Hive
Identity and Access Management
Python
Machine Learning
Neo4j
Open Source Technology
Performance Tuning
TensorFlow
Search Technologies
Data Streaming
Systems Integration
Workflow Management Systems
Enterprise Data Management
Parquet
Data Logging
Data Processing
Feature Engineering
PyTorch
Large Language Models
Spark
Containerization
Data Lake
PySpark
Gitlab-ci
Git Flow
Scikit Learn
Information Technology
Data Lineage
HuggingFace
Avro
Kafka
Machine Learning Operations
Terraform
Stream Processing
Data Pipelines
Jenkins
Databricks

Job description

Senior/Lead technical data architect to design, build, and operate enterprise data platforms that power GenAI and AI/ML use cases. This is a highly technical, hands-on role responsible for data platform architecture, endtoend data engineering, ML/LLM pipeline design, production model onboarding, and delivery of scalable Databricks- centric solutions across cloud environments. Candidate must be AWS Certified Machine Learning - Specialty.

What You'll Be Doing:

  • Architect and implement enterprise data platforms (batch + streaming) optimized for ML, LLMs, and GenAI workloads.
  • Lead design and hands on implementation of Databricks workspaces, Unity Catalog, Delta Lake design patterns, cluster policies, and performance tuning.
  • Build and own end to end data pipelines (ingest, transform, feature engineering, serving) using PySpark, Databricks Jobs, Spark SQL, Delta Lake, and orchestration tools.
  • Design and operationalize model training, fine tuning (LLM), evaluation, deployment, and monitoring pipelines (MLOps/RAG/CAG) integrating Databricks MLflow, CI/CD, and infra-as-code.
  • Implement vectorless and vectorization/embedding pipelines, vector store integrations, and retrieval layers for RAG (FAISS, Pinecone, Weaviate, Milvus).
  • Define data schemas, governance, lineage, access controls, and data product APIs; implement Unity Catalog or equivalent for centralized governance.
  • Drive cost/performance optimization for storage, compute (spot/preemptible),and query patterns.
  • Collaborate with engineers, data scientists, product owners, and security to translate business needs into production GenAI solutions.
  • Mentor and lead engineering teams; conduct architecture reviews, code reviews, and run technical deep dives.
  • Implement observability for data and ML pipelines (metrics, logging, data quality tests, alerting).
  • Create reproducible experiment tracking, model registry, and rollout strategies (canary, shadow testing, rollback).
  • Stay current on GenAI/LLM architectures and evaluate/introduce new tooling and frameworks.

Requirements

  • BA or BS degree in CS, Computer Engineering, Information Technology or a related field.

  • 8+ years hands on experience in data engineering/platform architecture; 3+ years in an architect or lead role.

  • Proven, hands on Databricks experience (designing workspaces, Delta Lake, performance tuning, productionizing Spark jobs).

  • Deep Spark + PySpark expertise and experience with Databricks Runtime.

  • Strong experience building ML/LLM pipelines and operationalizing models (training, fine tuning, serving).

  • Practical experience with vector embeddings, semantic search, and RAG architectures.

  • Solid Python expertise and common ML libraries (PyTorch, TensorFlow, Hugging Face transformers) and MLflow.

  • Cloud platform experience (AWS strongly preferred).

  • Experience with containerization and orchestration while leveraging open source libraries for unstructured and structured data processing, serving/inference.

  • Strong SQL skills; experience with distributed query/warehouse systems and parquet/AVRO/Delta formats.

  • CI/CD and infra-as-code experience (Terraform, GitOps, Jenkins/GitHub Actions/GitLab CI).

  • Data governance, security, and IAM experience; experience implementing row/column level access controls and data lineage.

  • Demonstrated ability to design for scalability, reliability, and cost efficiency.

Preferred Qualifications:

  • Prior experience with Databricks Unity Catalog, Photon, and Databricks SQL.
  • Experience integrating Databricks with vector databases (Pinecone, neo4j) and retrieval frameworks (LangChain, LlamaIndex).
  • Familiarity with AWS Bedrock or other managed LLM services.
  • Experience with realtime streaming (Kafka, Kinesis) and stream processing on Databricks Structured Streaming.
  • Certifications: Databricks Certified Professional.
  • Experience with data quality and profiling tools (Great Expectations, Soda).
  • Experience with large-scale ETL frameworks and tools (Airflow, Prefect)., Applicants must be authorized to work in the U.S. We may consider candidates currently in H-1B status who are eligible for transfer.

Benefits & conditions

The proposed salary range for this role is $160,000 to $190,000 USD. The salary range provided is a good faith estimate representative of all experience levels. Karsun considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate's work experience, location, education/training, and key skills.

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