Resident solution architect

Weekday AI; India; Vor Ort; INR 4.000.000 - 7.000.000; Vollzeit

Zusammenfassung

Weekday AI is seeking an experienced Resident Solutions Architect in India to join a client-focused team. You will design scalable Lakehouse architectures and lead technical engagements from proof-of-concept to production deployment.

The role demands strong hands-on data engineering skills, expert knowledge of Databricks, Spark/PySpark, SQL, and cloud integration across AWS, Azure or GCP. Excellent communication and customer-facing abilities are essential.

Qualifikationen

• Must have hands-on Databricks and Spark/PySpark experience.
• Strong SQL and data engineering fundamentals required.
• Experience designing data pipelines and data platforms.
• Experience with at least one cloud: AWS, Azure or GCP.

Aufgaben

• Work directly with enterprise customers as a trusted technical advisor.
• Translate customer requirements into scalable data architectures.
• Design modern Lakehouse/Data+AI architectures using Databricks.
• Develop and guide POCs, technical workshops and architecture demos.
• Work with Apache Spark, PySpark, SQL and Databricks extensively.
• Design scalable data ingestion, transformation and processing pipelines.
• Integrate Databricks with cloud platforms (AWS/Azure/GCP).
• Provide technical recommendations on data engineering, analytics and cloud architecture.

Kenntnisse

Databricks

Apache Spark / PySpark

SQL

Data Engineering fundamentals

Data pipelines

Cloud platforms (AWS/Azure/GCP)

Jobbeschreibung

This role is for one of Weekday’s clients
Min Experience: 9+ years
Location: India
JobType: full-time

We are looking for an experienced Resident Solutions Architect with strong expertise in Databricks, Apache Spark, Data Engineering and Cloud Data Platforms.

The RSA will work closely with enterprise customers to understand complex data and AI requirements, design scalable architectures, lead technical engagements and help customers successfully implement and productionize solutions on the Databricks platform.

The ideal candidate combines strong hands-on technical expertise with excellent customer-facing, consulting and communication skills.

Key Responsibilities

• Work directly with enterprise customers as a trusted technical advisor.
• Understand customer business and technical requirements and translate them into scalable data architectures.
• Design and implement modern Lakehouse / Data + AI architectures using Databricks.
• Develop and guide POCs, technical workshops and architecture demonstrations.
• Work extensively with Apache Spark / PySpark, SQL and Databricks.
• Design scalable data ingestion, transformation and processing pipelines.
• Work with Delta Lake, Unity Catalog and Databricks platform capabilities.
• Support data migration and modernization initiatives.
• Integrate Databricks with cloud platforms such as AWS, Azure or GCP.
• Troubleshoot performance, scalability, reliability and data pipeline issues.
• Work with customer engineering, data science, architecture and leadership teams.
• Provide technical recommendations around data engineering, analytics, ML/AI and cloud architecture.
• Create reference architectures, technical documentation and implementation guidance.
• Support production deployment and adoption of Databricks solutions.
• Conduct technical workshops, enablement sessions and knowledge-sharing activities.

Required Skills

Must Have

• Strong experience with Databricks
• Strong Apache Spark / PySpark
• Strong SQL
• Strong Data Engineering fundamentals
• Experience designing data pipelines / data platforms
• Experience with at least one cloud:
• AWS
• Azure
• GCP
• Strong customer-facing / consulting experience
• Architecture/design experience
• Excellent communication and presentation skills

Good to Have

• Delta Lake
• Unity Catalog
• MLflow
• Kafka
• Streaming
• Data migration
• MLOps
• Machine Learning
• GenAI
• Data governance
• Terraform / CI/CD
• Snowflake
• Hadoop ecosystem

Databricks' own Solutions Architect requirements similarly emphasize customer-facing architecture, big-data/data-science expertise, SQL, programming, cloud and the ability to translate customer needs into technical solutions.

Must-have skills

Data Engineering, Python, Solution Architecture

Good-to-have skills

SQL, azure, Databricks