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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and building AI/LLM data pipelines, optimizing production-grade RAG systems, and developing scalable data solutions using advanced tools and frameworks. Proficient in ensuring data quality, governance, and observability while collaborating with engineering teams to transition AI prototypes into production-ready systems.
Highest-signal resume keywords
Data EngineeringPython ProgrammingLLM Application DevelopmentDatabricksApache Spark
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data Pipeline DesignETL/ELT DevelopmentSQLRAG ArchitectureSemantic SearchVector RetrievalEmbedding GenerationModel EvaluationCost OptimizationData Governance
Tools & Technologies
DatabricksSnowflakeApache SparkDelta LakeAirflow
Industry Keywords
AILLMGenerative AIData QualityObservability
Tech Stack
Tools & technologiesAirflowApacheCloudDistributed SystemsETLJavaPythonScalaSparkSQL
About the role
Key responsibilities & impact- Design and build AI/LLM data pipelines supporting training, inference, evaluation, embeddings, and retrieval workloads
- Build production-grade RAG systems, including ingestion, chunking, embedding generation, indexing, retrieval, reranking, and context construction
- Develop AI applications using LLMs, structured outputs, function/tool calling, and agentic workflows
- Build and optimize semantic search and vector retrieval systems
- Develop frameworks for LLM evaluation, monitoring, tracing, quality measurement, latency, and cost optimization
- Design scalable batch and streaming pipelines using Databricks, Apache Spark, Delta Lake, Snowflake, and Airflow
- Build data products and platforms that make structured and unstructured enterprise data accessible to AI applications
- Develop reliable ETL/ELT pipelines and optimize large-scale distributed workloads for performance and cost
- Establish data quality, governance, lineage, security, and observability practices
- Partner with ML and application engineering teams to move AI prototypes into production-ready systems
- Help build the data and AI foundation for intelligent products and production-ready AI systems
Requirements
What you’ll need- 5+ years of experience in data engineering, software engineering, distributed systems, or a related field
- Strong programming skills in Python and/or Scala/Java and advanced SQL
- Hands-on experience with Databricks, Snowflake, Apache Spark, Delta Lake, and Airflow
- Strong experience with cloud data platforms such as Snowflake and/or Databricks
- Practical experience building applications using LLMs or Generative AI
- Strong understanding of RAG architectures, embeddings, vector databases, semantic search, and retrieval systems
- Familiarity with LLM concepts including prompting, structured outputs, tool calling, and model evaluation
- Experience designing scalable, reliable, and observable production data systems
Benefits
Comp & perks- Work from (almost) anywhere for up to 20 days per year
- Company-paid therapy sessions through SpringHealth
- Company-paid subscription to Headspace
- Annual company-wide week off a year
- Paid parental leave
- Generous paid vacation + time off for your birthday
- Paid volunteer time
- Development Dollars
- Leadership development
- Access to thousands of on-demand e-learnings
- Travel Discounts
- Employee Resource Groups
- Quarterly team offsites
- Tax optimisation options
- Generous health insurance
- Pension fund