Your Profile

We are looking for a Data Analytics Engineer who approaches data with a software engineering mindset but thinks like a scientist. You will join our Data Solutions squad to build and maintain production-grade data platforms.We are seeking proactive, inquisitive problem solvers who look well beyond the surface of a task. We want engineers who ask "why?", formulate hypotheses, and closely examine the data itself to understand underlying pipeline behaviors. You know that a pipeline's efficiency is directly tied to the nature of the data flowing through it, and you gather empirical evidence to guide your architectural decisions.While you understand the business context, your primary focus is technical and analytical: building scalable workflows, diagnosing complex data issues through evidence gathering, writing exceptional Python/SQL code, and optimizing cloud infrastructure.

Must-Have Skills

• Rigorous Educational Background: At least a B.Sc. (M.Sc. or Ph.D. strongly preferred) in Computer Science, Mathematics, Physics, Neuroscience, Economics, or another empirical science field that emphasizes the scientific method and evidence-based problem solving.
• Experience: 2+ years of experience specifically in Analytics Engineering, Data Engineering, or backend development heavily focused on data.
• Scientific & Analytical Mindset: Proven ability to work hypothesis-driven, dissecting logs, telemetry, and raw data to solve complex problems and explain unexpected pipeline behaviors.
• Excellent Python Proficiency: You have deep experience developing and deploying production-grade Python code. You are highly skilled in both object-oriented and functional programming paradigms, write modular code, utilize advanced testing libraries, and deeply understand exception handling, logging, and system optimization.
• Advanced SQL & dbt: Exceptional ability to build scalable data models (Jinja templating, macros, incremental strategies). You have a deep understanding of query execution plans and a track record of rigorous SQL query optimization.
• Agentic AI Proficiency: Demonstrated ability to smartly and responsibly utilize agentic AI coding assistants to accelerate development and architecture—this is a core requirement, not a novelty.
• Software Engineering Fundamentals: Hands-on experience with Git flows, CI/CD pipelines (e.g., GitHub Actions, GitLab CI), and Containerization (Docker).
• AWS Cloud Native Experience: Experience building and maintaining data workflows using serverless architectures such as AWS Lambda, StepFunctions, Glue, and Athena.
• Testing Mindset: Experience implementing Unit Tests and Integration Tests for data pipelines rather than relying solely on manual checks.
• Data Warehouse Ops: Solid understanding of warehousing architecture (Snowflake, Redshift, or BigQuery), including partitioning and clustering concepts.

Nice to Have

• Infrastructure as Code: Experience with Terraform to manage cloud resources.
• Orchestration: Experience with modern data orchestration tools like Airflow, Dagster, or Prefect.
• Big Data: Knowledge of big data processing frameworks (Spark/PySpark).
• Dashboarding: Familiarity with visualization tools (Streamlit, Preset, Tableau, etc.) primarily used for debugging and system monitoring.