Data Analytics Engineer

Intro: At Adsquare, our mission is driven by our core focus: Passion – Solving complex challenges with great people, tech, and data. Niche – Location Intelligence for Programmatic Advertisers. Our core values: Drive – We turn ambition into actionResilience – We adapt, persevere, and grow strongerNo BS – We value honesty, transparency, and clear communicationHumble – We let results speak for themselvesMoral Compass – We do the right thing with fairness, integrity, and respect Your Mission You will join our Data Solutions squad to build and maintain production-grade data platforms. This is not a Data Analyst role — your primary focus is technical: building scalable workflows, writing clean and testable Python/SQL code, automating deployments, and supporting cloud infrastructure optimisations. Key Responsibilities Scientific Problem Solving & Deep Dives: Act as an investigative engineer. Formulate hypotheses and dive deep into data, logs, and telemetry to understand analysis results, explain unexpected anomalies, and gather evidence. You won't just move data; you will scrutinize it to understand what is being processed (e.g., identifying when we are processing useless data that drives up costs).Pipeline Engineering: Build, deploy, and maintain robust transformation pipelines for high-volume data spanning the full lifecycle: ingestion, transformation, testing, deployment, and monitoring.Optimization & SQL Mastery: Write highly efficient code and relentlessly optimize SQL queries. You will analyze query execution plans, refactor legacy systems, eliminate redundancies, and improve pipeline efficiency to reduce cloud compute costs (e.g., optimizing Athena/Snowflake/Redshift clustering or AWS Glue jobs).AI-Augmented Engineering: Responsibly and intelligently leverage agentic AI tools (CLI or IDE-based) as core instruments in your daily workflow for more efficient planning, architecting, and implementation of features.Data Quality & Observability: Focus on infrastructure monitoring and telemetry rather than just business dashboards. Implement robust alerts and checks (e.g., dbt tests, Great Expectations) to catch data quality issues at the source.Software Engineering Best Practices: Adhere to and promote technical rigor using CI/CD workflows, containerization (Docker), and automated testing. Collaborate with Senior Engineers on architecture and code reviews. 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. Yearly OTE: €60,000 – €75,000 What We Offer Hybrid + remote from anywhere up to 3 months/year€1,200 yearly learning & development budget30 vacation daysUrban Sports Club membership + company pension schemeLatest hardware providedRegular team & company events Recruiting Process For technical roles (Data, Engineering, and Product), candidates are encouraged to upload any technical certifications referenced in their CV via the "Other Documents" section of the application form. Alternatively, certification documents may be submitted after the first technical interview. Any certifications listed on the CV must be provided and successfully validated before proceeding to the second technical interview stage. Step 1: Online quizzes (45 mins).Step 2: Value-based interview (30 mins).Step 3: Deep-dive technical interview (1.5 hours) with the Data team.Step 4: Practical data-crunching challenge.Step 5: Team Meet & Greet — the final step to ensure we're a great fit for each other. Berlin | Hybrid | Start: ASAP