Databricks Architect

Role Overview

The Databricks Architect is responsible for designing, implementing, and optimizing scalable data engineering and analytics solutions on the Databricks Lakehouse Platform on AWS. This role requires deep expertise in distributed data processing, Delta Lake–based architectures, and modern data engineering best practices. The architect will partner with cross‑functional teams to define data strategies, ensure platform reliability, and enable advanced analytics, ML, and BI workloads across the enterprise.

Must Demonstrate (Critical Architectural Capabilities)

1.     Designing Databricks-based Lakehouse architectures on AWS

2.     Clear separation of compute layer vs. serving layer

3.     Low-latency API/data delivery strategy (cannot rely solely on Spark)

4.     Caching strategies for performance acceleration and cost efficiency

5.     Data partitioning and optimization strategy, including file-size tuning

6.     Ability to handle multi-terabyte structured time-series datasets

7.     Skill in distilling architectural significance from complex requirements

8.     Strong curiosity and requirement‑probing mindset

9.     Player‑coach leadership style (hands-on engineering + design guidance)

Key Responsibilities

Architecture & Design

1.     Architect end-to-end Databricks Lakehouse solutions on AWS for ingestion, processing, storage, and consumption.

2.     Define and implement Delta Lake patterns including Medallion Architecture (Bronze/Silver/Gold).

3.     Lead design of scalable data pipelines using PySpark, Spark SQL, Workflows, and Delta Live Pipelines.

4.     Architect solutions for structured, semi-structured, and time-series workloads.

5.     Ensure architectures support low-latency delivery, serving‑layer separation, and high performance.

Engineering & Implementation

1.     Build robust ETL/ELT pipelines using Databricks Notebooks, Jobs, and Workflows.

2.     Implement streaming and incremental data processing as needed using Structured Streaming.

3.     Optimize Spark jobs with partitioning, caching, ZORDER, file compaction, and shuffle reduction.

4.     Implement CI/CD automation using Databricks Repos, GitLab/GitHub, and Infrastructure-as-Code.

AWS Cloud & Platform Expertise

1.     Architect Databricks solutions using AWS-native services including:

2.     S3 (data storage)

3.     Glue Catalog (metadata governance)

4.     IAM (identity & access control)

5.     Lambda / API Gateway (low-latency serving mechanisms)

6.     Kinesis (streaming ingestion)

7.     Ensure security, governance, and compliance via Unity Catalog, RBAC, and encryption standards.

8.     Monitor workloads and optimize cluster sizing, autoscaling, and cost controls.

Collaboration & Leadership

1.     Partner with data engineers, ML engineers, BI teams, and business stakeholders.

2.     Serve as a Databricks SME, defining best practices, standards, and architectural patterns.

3.     Conduct architectural reviews and guide teams on solution choices.

4.     Lead PoCs, evaluate new Databricks features, and drive platform adoption across teams.

Quality, Governance & Observability

1.     Define standards for data quality, lineage, observability, and operational governance.

2.     Implement automated testing frameworks for pipelines and notebooks.

3.     Establish monitoring dashboards, performance baselines, and reliability KPIs.

Required Skills & Experience

Technical Skills

1.     7+ years in data engineering or data architecture.

2.     3+ years hands-on with Databricks.

3.     Strong expertise in Spark, PySpark, SQL, and distributed data systems.

4.     Deep understanding of Delta Lake features (ACID, OPTIMIZE, ZORDER, Auto Loader).

5.     Experience with workflows/orchestration and Databricks REST APIs.

6.     Hands-on expertise with AWS, specifically:

7.     S3

8.     Glue / Glue Catalog

9.     IAM

10.   Lambda

11.   Kinesis

12.   Familiarity with CI/CD, Git, DevOps, and IaC (Terraform preferred).

13.   Soft Skills

14.   Strong analytical and problem‑solving abilities.

15.   Excellent communication and stakeholder management.

16.   Ability to lead design discussions and guide engineering teams.

17.   Strong documentation and architectural blueprinting abilities.

Preferred Qualifications

1.     Databricks Certifications such as:

2.     Databricks Certified Data Engineer Professional

3.     Databricks Certified Machine Learning Professional

4.     Databricks Lakehouse Fundamentals

5.     Experience with MLflow, Feature Store, or MLOps pipelines.

6.     Experience in regulated industries (BFSI, healthcare, etc.).