🚀 Who Are We?
Welcome to Welltech—where health meets innovation! 🌍 As a global leader in Health & Fitness industry, we’ve crossed over 200 million installs with three life-changing apps, all designed to boost well-being for millions. Our mission? To transform lives through intuitive nutrition trackers, powerful fitness solutions, and personalized wellness journeys—all powered by a diverse team of over 700 passionate professionals with presence across 5 hubs.
Why Welltech? Imagine joining a team where your impact on global health and wellness is felt daily. At Welltech, we strive to be proactive wellness partners for our users, while continually evolving ourselves.
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.
You Should Have:
4+ years of experience in data engineering or backend development, with a strong focus on building production-grade data pipelines.
Solid experience working with AWS services (Redshift, 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.
