Data Analytics Engineer

At Midas, we are working on real-life engineering challenges to transform the world of finance. We’ve transformed investing in Turkey by delivering a seamless experience for everyday investors. Today, 3.5 million users invest with Midas. Backed by an $80M Series B, the largest fintech investment ever in Turkey, we are scaling faster than ever. At Midas, decisions move fast — and they're only as good as the data behind them. This role owns the decision models and data pipelines that power all the products in Midas, ensuring every action taken is measurable, every line of data is trustworthy, and every insight arrives accurately on time. Join the Midas team and help shape the future of investing for over 3.5 million users — in a high-velocity, product-driven engineering culture. Data Engineering at Midas operates at scale — hundreds of decisions that compound daily. To move fast without breaking things, we need data infrastructure that's accurate, automated, and built for modeling from the ground up. As a Data Analytics Engineer at Midas, you’ll collaborate with data scientists, analysts, and product teams to design, develop, and manage data pipelines for Machine Learning, Business Intelligence, and Data Analytics needs. The role involves creating efficient data models, optimizing performance, and documenting data processes to ensure clarity and maintainability across cross-functional teams. About Midas Team

We solve challenging problems and build 10x better products.

We expect hard work, high ownership, a strong desire to learn. 

We bring the best people, holding high quality standards, and an environment of speed and ambition.

You will get to push your boundaries and learn from the best.

Results Expected

Achieve >99% data accuracy - measured over rolling 90-day periods - for all metrics, validated through automated testing and monitoring

Build and maintain data pipelines and integrations that feed customer-facing products, data science models, critical decisive analyses and executive reports — with zero recurring incidents that could impact data-driven decisions

Design and develop the semantic layer with an engineering perspective that enable rapid analysis and experimentation, reducing time-to-result by 50%+ within 6 months through automated data collection and analysis

Implement full observability across data pipelines — monitoring, alerting, and automated anomaly detection — with <1 hour mean time to detection for data quality issues

In collaboration with cross-functional teams, including business stakeholders, design and implement data-driven solutions to business problems

Document data engineering processes, data models, and data flows to ensure clarity, consistency, and maintainability of data solutions.

Technical Competencies

Builds robust, tested data pipelines using SQL, Python, and orchestration tools like Airflow (but remains tool-agnostic and adaptable)

Designs data models that support causal inference, experimentation, and self-service data onboarding for all parties

Writes clean, version-controlled code and integrates data quality checks into every pipeline

Thinks in systems — designs for monitoring, debugging, and long-term maintainability from day one

Cultural Competencies

Takes full ownership — doesn't wait, just solves.

Questions assumptions and rebuilds from first principles.

Balances speed and quality — ships fast, but always clean.

Collaborates fluidly across teams; communicates clearly, with empathy and precision.