Resident Solution Architect – Data, Databricks

🔥 0 minutes ago

🇮🇳 India – Remote

💵 ₹4M - ₹7M / year

⏰ Full Time

🟠 Senior

🔴 Lead

💻 Solutions Engineer

👻 Ghost score 20%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Weekday (YC W21)

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2021

💼 Consulting

👥 HR Tech

☁️ SaaS

Consulting • HR Tech • SaaS

Weekday is a modern recruitment platform that combines AI technologies with a vast database of potential candidates, aiming to streamline the hiring process for companies in India. They offer various services, including a proactive outreach approach that helps employers connect with top talent, as well as tools for candidates to easily apply for jobs. Weekday's emphasis on candidate engagement through multiple channels, including email, WhatsApp, and phone calls, sets it apart in the competitive landscape of recruitment agencies.

📋 Description

• Design end-to-end Data, Analytics, and Lakehouse solutions using Databricks • Develop and review scalable ETL/ELT pipelines and enterprise data platforms • Work hands-on with Databricks, Apache Spark, PySpark, Python, and SQL • Design solutions using Delta Lake, Unity Catalog, Databricks SQL, and Databricks Workflows • Develop scalable data architectures across Azure, AWS, and GCP environments • Conduct technical discovery sessions, architecture workshops, and solution discussions with enterprise customers • Translate business and technical requirements into scalable architecture and implementation approaches • Prepare High-Level Designs, Low-Level Designs, architecture diagrams, technical proposals, and solution documentation • Lead POCs, technical demonstrations, solution validations, and architecture assessments • Provide technical guidance, mentoring, and architectural direction to Data Engineering teams • Review data pipelines, architecture designs, code, and implementation approaches for scalability and maintainability • Troubleshoot and optimise Databricks and Spark workloads for performance, scalability, reliability, and cost efficiency • Support data platform modernisation, migration, and transformation initiatives • Collaborate with Sales, Pre-Sales, Delivery, Product, and Engineering teams on technical solutioning • Engage senior customer stakeholders to communicate architecture decisions, technical recommendations, risks, and trade-offs • Identify opportunities to improve data platform architecture, engineering practices, automation, and operational efficiency • Stay current with Databricks, cloud data platforms, distributed computing, and modern data engineering technologies

🎯 Requirements

• 8+ years of experience in Data Engineering, Data Architecture, Solution Architecture, Big Data, or a closely related field • Strong hands-on expertise in Databricks and enterprise data platforms • Strong knowledge of Apache Spark and PySpark • Advanced programming skills in Python and SQL • Strong understanding of Lakehouse Architecture, Delta Lake, Unity Catalog, Databricks SQL, and Databricks Workflows • Proven experience designing scalable ETL/ELT pipelines, data platforms, data models, and data warehouses • Experience working with at least one major cloud platform: Azure, AWS, or GCP • Strong understanding of distributed data processing, scalability, reliability, and data platform architecture • Hands-on experience with performance tuning and optimisation of Databricks and Spark workloads • Proven experience in technical solution design, architecture workshops, POCs, technical demonstrations, and solution validation • Strong customer-facing consulting experience with the ability to engage Architects, CTOs, CDOs, Engineering Managers, and senior technology stakeholders • Excellent communication, presentation, stakeholder-management, and technical storytelling skills • Ability to provide technical leadership and mentorship to Data Engineering teams • Strong analytical and problem-solving skills with a structured approach to complex technical challenges • Databricks certification would be an advantage • Exposure to Snowflake, Kafka, Spark Streaming, dbt, Terraform, CI/CD, MLflow, MLOps, GenAI, LLM/RAG, or data migration is desirable • Strong ownership mindset with the ability to work independently in a remote, customer-facing environment