Senior Data Engineer (Redshift)

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Наша компания

Who Are We? Welltech is a global wellness technology company with Ukrainian roots. Our mission is to build and scale wellness apps globally through state-of-the-art, tech-driven performance marketing.

О роли

We are one of the most established players in the wellness app space, and we are accelerating. Over 25.5 million people across the world use our apps — Muscle Booster, Yoga-Go, and WalkFit — to build healthier habits, move more, and feel better every day. Every subscription represents a real person making a real change in their life, and we take that seriously. With 500+ people across hubs in Cypr

Наши требования

As a Senior Data Engineer, you will play a crucial role in building and maintaining the foundation of our data ecosystem. You’ll work alongside data engineers, analysts, and product teams to create robust, scalable, and high-performance data pipelines and models. Your work will directly impact how we deliver insights, power product features, and enable data-driven decision-making across the company

This role is perfect for someone who combines deep technical skills with a proactive mindset and thrives on solving complex data challenges in a collaborative environment

Experience with additional AWS services: EMR, EKS, Athena, EC2

Hands-on knowledge of alternative data warehouses like Snowflake or others

Experience with PySpark for big data processing

Familiarity with event data collection tools (Snowplow, Rudderstack, etc.)

Interest in or exposure to customer data platforms (CDPs) and real-time data workflows

Candidate journey: ⭕️ Recruiter call ➔ ⭕️ Technical call with the hiring manager ➔ ⭕️ Meet the future stakeholders

Check out some of our products

4+ years of experience in data engineering or backend development, with a strong focus on building production-grade data pipelines

2-3+ years of experience working with AWS services (Administration of Redshift is a must)

Solid experience working with AWS services (Spectrum, S3, RDS, Glue, Lambda, Kinesis, SQS)

Proficient in Python and SQL for data transformation and automation

Experience with dbt for data modeling and transformation

Good understanding of streaming architectures and micro-batching for real-time data needs

Experience with CI/CD pipelines for data workflows (preferably GitLab CI)

Familiarity with event schema validation tools/ solutions (Snowplow, Schema Registry)

Excellent communication and collaboration skills

Strong problem-solving skills—able to dig into data issues, propose solutions, and deliver clean, reliable outcomes

A growth mindset—enthusiastic about learning new tools, sharing knowledge, and improving team practices

Cloud: AWS (Redshift, Spectrum, S3, RDS, Lambda, Kinesis, SQS, Glue, MWAA)

Languages: Python, SQL

Orchestration: Airflow (MWAA)

Modeling: dbt

CI/CD: GitLab CI (including GitLab administration)

Monitoring: Datadog, Grafana, Graylog

Event validation process: Iglu schema registry

APIs & Integrations: REST, OAuth, webhook ingestion

Infra-as-code (optional): Terraform

Дополнительно

Pipeline Development and Optimization: Build and maintain reliable, scalable ETL/ELT pipelines using modern tools and best practices, ensuring efficient data flow for analytics and insights

Data Modeling and Transformation: Design and implement effective data models that support business needs, enabling high-quality reporting and downstream analytics

Collaboration Across Teams: Work closely with data analysts, product managers, and other engineers to understand data requirements and deliver solutions that meet the needs of the business

Ensuring Data Quality: Develop and apply data quality checks, validation frameworks, and monitoring to ensure the consistency, accuracy, and reliability of data

Performance and Efficiency: Identify and address performance issues in pipelines, queries, and data storage. Suggest and implement optimizations that enhance speed and reliability

Security and Compliance: Follow data security best practices and ensure pipelines are built to meet data privacy and compliance standards

Innovation and Continuous Improvement: Test new tools and approaches by building Proof of Concepts (PoCs) and conducting performance benchmarks to find the best solutions

Automation and CI/CD Practices: Contribute to the development of robust CI/CD pipelines (GitLab CI or similar) for data workflows, supporting automated testing and deployment