Data Analytics Engineer (m/f/d)

Über diese Stelle

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 are integral to everything we do: Drive: We turn ambition into action. Resilience: We adapt, persevere, and grow stronger.

No BS: We value honesty, transparency, and clear communication.

Humble: We choose modesty over vanity and let results speak for themselves.

Moral Compass: We do the right thing with fairness, integrity, and respect.

We seek candidates who not only bring excellent technical expertise but also embody these values in every aspect of their work.

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.

We are looking for a Data Analytics Engineer who approaches data with a software engineering mindset. You will join our Data Solutions squad to build and maintain production-grade data platforms.

This is not a Data Analyst role. While you will understand the business context, your primary focus is technical: building scalable workflows, writing clean and testable Python/SQL code, automating deployments, and supporting cloud infrastructure optimizations. You will ensure our pipelines remain reliable, cost-effective, and maintainable. We are looking for a candidate who has solid analytics engineering experience or a strong background in backend development focused on data.

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.

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…