Databricks Solution Architect

Job not on LinkedIn

🔥 0 minutes ago

🇮🇳 India – Remote

⏰ Full Time

🟠 Senior

🔴 Lead

💻 Solutions Engineer

👻 Ghost score 25%

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 engineering and analytics solutions using Databricks Lakehouse architecture • Define scalable architectures for ingestion, transformation, storage, orchestration, analytics, and downstream consumption • Develop architecture patterns using Apache Spark, PySpark, SQL, Delta Lake, Unity Catalog, and Databricks services • Design and guide batch and streaming pipelines, ETL/ELT frameworks, data models, and enterprise data integration solutions • Evaluate data platforms and recommend migration, modernization, and performance optimization strategies • Establish architecture standards, reusable design patterns, engineering best practices, and implementation guidelines • Guide data engineering teams on technical design, code quality, scalability, reliability, and performance tuning • Participate in POCs, architecture reviews, technical troubleshooting, and production deployments • Engage clients to understand business objectives, data ecosystems, technical challenges, and requirements • Lead technical discovery sessions, requirements workshops, and solution architecture discussions • Translate business requirements into technical solutions, architecture diagrams, implementation approaches, and delivery roadmaps • Prepare technical proposals, solution presentations, RFP/RFI responses, and Statements of Work with sales and delivery teams • Present architecture recommendations, technology choices, implementation strategies, assumptions, dependencies, and risks • Assess technical feasibility and recommend technologies based on scalability, security, and cost • Support solution handover from presales to implementation teams • Estimate project effort, timelines, resource requirements, implementation costs, Databricks consumption, and cloud infrastructure costs • Determine team composition and resource allocation across architecture, data engineering, cloud engineering, QA, and related functions • Identify cost drivers including DBUs, compute sizing, storage, networking, frequency, concurrency, and processing requirements • Identify cost optimization opportunities and provide technical and commercial inputs for pricing and budgeting • Design and implement Databricks solutions on Azure, AWS, or GCP • Evaluate cloud-native services for ingestion, storage, orchestration, security, monitoring, and integration • Define data access, security, governance, and compliance considerations • Optimize compute utilization, workload execution, storage consumption, and cloud spending • Support cloud data platform migrations and modernization initiatives • Review technical designs and implementation plans for architecture and business alignment • Identify risks, dependencies, and implementation challenges and recommend mitigations • Mentor engineering teams on Databricks development, cloud architecture, and performance optimization • Coordinate client stakeholders, presales teams, architects, and engineering teams throughout the project lifecycle

🎯 Requirements

• Minimum 7+ years of experience • 8–12+ years of overall experience in Data Engineering, Data Architecture, Cloud Data Solutions, or related disciplines • Strong hands-on experience designing and implementing enterprise-grade solutions on Databricks • Deep understanding of Lakehouse principles, Delta Lake, data storage, processing, governance, and analytics architecture • Strong expertise in Apache Spark, PySpark, SQL, distributed processing, and performance tuning • Experience designing scalable ETL/ELT pipelines, data models, and data integration frameworks • Strong understanding of Unity Catalog, data platform architecture, and enterprise data engineering practices • Hands-on experience with at least one major cloud ecosystem: Azure, AWS, or GCP • Experience with cloud data services, orchestration frameworks, APIs, and enterprise data platforms • Ability to estimate project effort, resource requirements, implementation timelines, and platform infrastructure costs • Experience with client-facing technical consulting, solution architecture, presales, and proposal development • Ability to create architecture diagrams, technical proposals, implementation roadmaps, and cost estimates • Strong analytical, presentation, stakeholder-management, and problem-solving skills • Experience defining data governance frameworks, data ownership, cataloging, lineage, and governance policies • Understanding of role-based access control, identity management, permissions, and secure data access • Knowledge of data protection, encryption, access policies, regulatory compliance, and cloud security best practices • Exposure to enterprise data migration, legacy platform modernization, and large-scale data transformation programs • Familiarity with machine learning, Generative AI, and Databricks AI capabilities • Experience with consulting firms, system integrators, cloud service providers, or data engineering service organizations • Knowledge of cloud pricing models, platform consumption metrics, FinOps principles, and infrastructure cost optimization • Relevant Databricks or cloud certifications are preferred • Candidates should provide total professional experience and relevant Databricks experience, cloud expertise, delivered data solutions, presales and proposal experience, estimation and cost-calculation experience, certifications, notice period, and availability