Senior Data Engineer (Redshift)
- Role
- Data Engineering
- Experience
- Senior
- Employment
- Full-time
- Company size
- Mid-size
Open to CY only. Set where you work from to check your eligibility.
No BS summary
Senior Data Engineer with 4+ years of experience in data engineering or backend development, and 2-3+ years working with AWS services, specifically Redshift administration. Must be proficient in Python and SQL, with experience in dbt and CI/CD pipelines. Strong problem-solving and communication skills are essential.
Core skills
Required skills
Optional skills
Required languages
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.
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 Cyprus, Ukraine, Poland, Spain, and the UK, we combine the scale of a market leader and the drive of a team that's just getting started.
What We're Looking For
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.
Challenges You’ll Meet:
- 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.
Required skills:
- 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.
Tech Stack You’ll Work With:
- 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
Bonus Points / Nice to Have:
- 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
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What you'll do
- Build and maintain reliable, scalable ETL/ELT pipelines using modern tools and best practices, ensuring efficient data flow for analytics and insights.
- Design and implement effective data models that support business needs, enabling high-quality reporting and downstream analytics.
- Work closely with data analysts, product managers, and other engineers to understand data requirements and deliver solutions that meet the needs of the business.
- Develop and apply data quality checks, validation frameworks, and monitoring to ensure the consistency, accuracy, and reliability of data.
- Identify and address performance issues in pipelines, queries, and data storage. Suggest and implement optimizations that enhance speed and reliability.
- Follow data security best practices and ensure pipelines are built to meet data privacy and compliance standards.
- Test new tools and approaches by building Proof of Concepts (PoCs) and conducting performance benchmarks to find the best solutions.
- Contribute to the development of robust CI/CD pipelines (GitLab CI or similar) for data workflows, supporting automated testing and deployment.
What they require
- 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.
Health & Fitness