Software developer (Data Engineer)

CustomerLabs is a fast-growing B2B SaaS company helping businesses maximize the value of their first-party customer data. Our platform helps marketers collect, unify, and activate customer data to improve marketing performance and drive measurable growth.

You'll build the data infrastructure behind CustomerLabs' marketing platform. The role covers scalable pipelines, warehouses, data models, quality, monitoring, and real-time data processing so teams can reliably work with high-volume marketing data.

·Design, build, and maintain scalable ETL/ELT pipelines and data-ingestion frameworks.

·Collect and process data from databases, APIs, files, and streaming sources.

·Build and optimize data warehouses and data-lake solutions.

·Create data models and schemas for analytics, attribution, customer journeys, and campaign performance.

·Implement data transformation, cleaning, governance, monitoring, and alerting.

·Ensure data quality, integrity, security, privacy, and compliance across marketing data systems.

·Troubleshoot pipeline issues, perform performance tuning, and support capacity planning.

·Collaborate with Data Scientists, Analysts, Product Managers, and business stakeholders.

·3+ years of experience in data engineering or a related role.

·Hands-on experience building ETL/ELT pipelines.

·Experience with a cloud data platform, specifically GCP.

·Strong Python and SQL skills; Golang is preferred.

·Experience with PostgreSQL, MySQL, and Redis.

·Experience with tools such as Spark, Kafka, Airflow, DBT, or Data form.

·Experience with GCP services including BigQuery and related data services.

·Working knowledge of Git/GitHub and Docker.

·Strong problem-solving, analytical thinking, communication, and collaboration.

·Real-time data processing and streaming experience.

·Knowledge of machine-learning pipelines and MLOps.

·Experience with data governance or data catalogue tools.

·Familiarity with BI tools such as Tableau, Power BI, Looker, Superset, or Metabase.

·Experience using AI tools such as Cursor, Claude, Copilot, ChatGPT, or Gemini to accelerate development.

·Pipelines are reliable, scalable, observable, and easy to maintain.

·Data is accurate and available for analytics and product use cases.

·Performance and reliability improve as data volume and complexity grow.

Welcome to the Average Kootam — A place where you’re trusted to own your work, make decisions, learn from the experience, and keep moving forward. You’ll know what you’re working towards, have the freedom to grow into bigger responsibilities, and see the impact of what you do. Teams here don’t work in isolation Sales, Marketing, Design, Engineering, and others work closely, challenge ideas, and build better things together. And when we’re not building, we make time to learn, connect, have fun, and bring the whole Kootam together.

Wanna know more about us? Connect with us:

Want to bypass all this and get a coffee with us at our office? Let us know here!