Concord, CA
Onsite
This role requires working with large, multi-terabyte datasets, advanced analytics, and end‑to‑end ML lifecycle management using Databricks, Python, PySpark, and AWS-native services.
Must Demonstrate (Critical Competencies)
Designing Databricks‑based lakehouse architectures on AWS (Delta Lake + S3 + Unity Catalog).
Clear separation of compute vs. serving layers in distributed architectures.
Caching strategies to accelerate reads and reduce compute cost.
Data partitioning, file size tuning, and optimization strategies for large-scale pipelines.
Experience handling multi-terabyte structured time‑series workloads.
Ability to distill architectural significance from ambiguous business requirements.
Strong curiosity, questioning, and requirement‑probing mindset.
Player‑coach approach: hands-on technical depth + ability to guide design.
Key Responsibilities
AI/ML & Advanced Analytics
Develop, train, and optimize ML models using Python, PySpark, MLflow, and Databricks Machine Learning.
Conduct exploratory data analysis (EDA) to identify patterns, trends, and insights in large datasets.
Deploy ML models into production using MLflow, Databricks Workflows, or other MLOps pipelines.
Build analytics solutions such as forecasting, anomaly detection, segmentation, or recommendation systems.
Design ML architectures aligned with Databricks Lakehouse on AWS.
Data Engineering & Lakehouse Architecture
Architect and build scalable ETL/ELT pipelines using PySpark, SQL, and Databricks Workflows.
Implement Delta Lake best practices, including OPTIMIZE, ZORDER, partitioning, and schema evolution.
Design lakehouse layers (Bronze/Silver/Gold) with strong separation of compute and serving layers.
Optimize cluster performance and jobs using Spark tuning, caching, and shuffle minimization.
Work with multi-terabyte, time-series, high‑velocity data in a distributed environment.
Ensure robust data availability for downstream ML and analytics workloads.
AWS Cloud Integration
Architect end-to-end data and ML solutions using AWS services, including:
S3 for storage
IAM for identity & access
Glue Catalog for metadata management
Networking for secure, high‑throughput data movement
Integrate Databricks with AWS-native compute, API layers, and low-latency endpoints.
Business Collaboration & Leadership
Translate business problems into scalable analytical or ML architectures.
Communicate complex statistical and architectural concepts to non‑technical stakeholders.
Collaborate with product, engineering, and business leaders to drive data-informed initiatives.
Provide design leadership while remaining hands-on in execution.
Skills & Qualifications
Required
Bachelor’s or Master’s in Computer Science, Data Science, Engineering, Statistics, or related field.
10+ years of experience in data engineering, ML engineering, or AI/ML architecture roles.
Deep expertise in Databricks on AWS, including:
PySpark / Spark SQL
Databricks Notebooks
Delta Lake
Unity Catalog
MLflow
Databricks Jobs & Workflows
Strong programming ability in Python (pandas, numpy, scikit-learn).
Demonstrated experience with large-scale, multi-terabyte data processing.
Strong understanding of ML algorithms, distributed systems, and data optimization.
Preferred
Experience with MLOps and production deployment pipelines.
Strong grasp of AWS-native data and compute services.
Understanding of CI/CD using GitHub Actions, GitLab CI, or similar.
Familiarity with deep learning frameworks (TensorFlow, PyTorch).
Key Competencies
Strong analytical and problem-solving skills.
Ability to work in fast-paced, highly collaborative environments.
Excellent communication and presentation abilities.
Self-driven with exceptional attention to architectural detail
