Senior Data Engineer (Redshift)

Embedded Shishya

Gopalganj

Hybrid

INR 7,684,000 - 12,075,000

Full time

14 days+

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Job summary

Welltech is seeking a Senior Data Engineer to build scalable data pipelines and models that power analytics and product features. You will work with data engineers, analysts, and product teams to deliver robust, high-performance data solutions.

You will design and optimize ETL/ELT pipelines, ensure data quality, and collaborate across teams to meet business needs. A strong foundation in Python, SQL, and AWS Redshift is essential, with dbt and CI/CD experience highly valued.

Qualifications

4+ years of experience in data engineering or backend development with production-grade data pipelines.
2-3+ years of experience with AWS services (Redshift administration is a must).
Proficient in Python and SQL for data transformation and automation.
Experience with dbt for data modeling and transformation.
Strong understanding of streaming architectures and micro-batching.
Experience with GitLab CI/CD for data workflows.
Familiarity with event schema validation tools such as Snowplow or Schema Registry.
Excellent communication and collaboration skills with a growth mindset.

Responsibilities

Build and maintain reliable ETL/ELT pipelines and data models.
Collaborate with data analysts, product managers, and engineers to deliver data solutions.
Ensure data quality with validation, checks, and monitoring.
Optimize pipelines for performance, speed, and reliability.
Implement CI/CD practices for data workflows and automation.

Skills

Python

SQL

AWS Redshift

dbt

Data pipelines

GitLab CI

Data modeling

Streaming

Data quality

Tools

Airflow

Snowplow

Schema Registry

Terraform

Datadog

Grafana

Kinesis

REST APIs

Job description

Headquarters: Cyprus
URL: http://welltech.com

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.