Senior Databricks Engineer

LAKEORA is a Riyadh-based data and AI advisory firm and a pure-play Databricks partner. We build the governed data platforms that Saudi enterprises run their decisions on, going deep on one platform rather than wide across many, with governance designed in from the start. We are early-stage, flat, and deliberately small. You work directly with customers, own the architecture you design, and see it reach production. We like engineers who are direct, curious, and would rather get something right than get it agreed. What are we looking for? We're looking for a Senior Data Engineer who owns the full cycle of Databricks implementations: discovery, architecture, delivery, and the demo that wins the room. At Lakeora, engineers are client-facing by design. You'll work directly with client stakeholders, shape data platform strategies, and present technical demos that make the value tangible. Lean team, high visibility, real impact. Key Responsibilities • Design and build scalable data pipelines using Spark (PySpark and SQL) on Databricks • Lead or support technical discovery sessions with prospective clients, documenting findings and shaping solution approaches • Deliver tailored technical demos on the Databricks platform, speaking to both business value and technical depth • Translate client requirements into delivery scopes and effort estimates in collaboration with the broader team • Act as technical lead across client engagements, from architecture design through implementation • Apply sound engineering practices: code reviews, unit testing, Git, and Agile delivery • Mentor junior engineers and contribute to raising the team's technical standard • Stay current on emerging trends and best practices in Databricks, Data, GenAI and AI. Qualifications Required: • 5+ years of data engineering experience with hands-on Spark development (PySpark and SQL) • Strong SQL fundamentals, conceptually not just syntax • Deep understanding of core Databricks concepts such as : Lakehouse, Medallion, Delta Lake and Unity Catalog • Hands-on Databricks experience including: Lakeflow Jobs, Declarative Pipelines, AI/BI Dashboards, and Genie • Cloud experience on one or more hyperscalers (Azure, AWS, or GCP) • Solid understanding of data warehousing, data modeling, ETL/ELT design, and pipeline architecture • Strong communication skills with the ability to engage both technical and business stakeholders confidently • Experience with version control (Git) and CI/CD tools • Degree in Computer Science, Software Engineering, Mathematics, or a related STEM field Preferred: • Databricks certifications (Data Engineer Associate / Professional strongly preferred) • Consulting or client-facing delivery experience • Experience with streaming technologies such as Kafka or Spark Streaming • Exposure to manufacturing, supply chain, or logistics data domains • Exposure to Data & AI regulations in Saudi Arabia • Exposure to AI and GenAI (Claude Code, RAG, Agents, ..etc) If this sounds like the kind of work you want to do, we would like to hear from you. LAKEORA is an equal opportunity employer and assesses every application on merit. Show more Show less

5+ years of data engineering experience with hands-on Spark development (PySpark and SQL); Strong SQL fundamentals, conceptually not just syntax; Deep understanding of core Databricks concepts such as : Lakehouse, Medallion, Delta Lake and Unity Catalog; Hands-on Databricks experience including: Lakeflow Jobs, Declarative Pipelines, AI/BI Dashboards, and Genie; Cloud experience on one or more hyperscalers (Azure, AWS, or GCP); Solid understanding of data warehousing, data modeling, ETL/ELT design, and pipeline architecture; Strong communication skills with the ability to engage both technical and business stakeholders confidently; Experience with version control (Git) and CI/CD tools; Degree in Computer Science, Software Engineering, Mathematics, or a related STEM field

Design and build scalable data pipelines using Spark (PySpark and SQL) on Databricks; Lead or support technical discovery sessions with prospective clients, documenting findings and shaping solution approaches; Deliver tailored technical demos on the Databricks platform, speaking to both business value and technical depth; Translate client requirements into delivery scopes and effort estimates in collaboration with the broader team; Act as technical lead across client engagements, from architecture design through implementation; Apply sound engineering practices: code reviews, unit testing, Git, and Agile delivery; Mentor junior engineers and contribute to raising the team's technical standard; Stay current on emerging trends and best practices in Databricks, Data, GenAI and AI.