Data Engineer Data Quality & Platform (WFH)

About the job:Or Client is building a real-time data platform that powers core product decisions and customer-facing systems.This role is for engineers who:Own data pipelines end-to-endCare deeply about data correctnessCan debug real production issues, not just monitor systemsYoull work on high-scale, event-driven pipelines and ensure data is reliable, accurate, and trusted across the business.

ResponsibilitiesOwn and operate data pipelines (ingestion transformation serving)Debug and resolve data issues in production (latency, inconsistencies, failures)Build scalable pipelines using Spark, Airflow, DBT, Kafka/CDCEnsure data quality and reliability across systemsWork closely with product and analytics teams to ensure correct business metricsImprove automation, monitoring, and observability of data systems

Ideal ProfileYou have atleast 4 years' experience in data engineering / data platform rolesStrong hands-on experience with SQL (advanced), Spark / PySpark and Airflow (or similar)Experience building and debugging production-grade data pipelinesStrong understanding of Data modeling, ETL/ELT systems and Data quality challengesOwnership mindset, someone who fixes problems end-to-end

Why Join UsHigh ownership- you build, run, and improve what you shipOpportunity to shape data reliability and platform foundationsWho can apply:Only those candidates can apply who:have minimum 4 years of experienceSalary:Competitive salaryExperience:4 year(s)Deadline:[hidden]:59:59

Responsibilities

Or Client is building a real-time data platform that powers core product decisions and customer-facing systems.This role is for engineers who:Own data pipelines end-to-endCare deeply about data correctnessCan debug real production issues, not just monitor systemsYoull work on high-scale, event-driven pipelines and ensure data is reliable, accurate, and trusted across the business.

ResponsibilitiesOwn and operate data pipelines (ingestion transformation serving)Debug and resolve data issues in production (latency, inconsistencies, failures)Build scalable pipelines using Spark, Airflow, DBT, Kafka/CDCEnsure data quality and reliability across systemsWork closely with product and analytics teams to ensure correct business metricsImprove automation, monitoring, and observability of data systems

Ideal ProfileYou have atleast 4 years' experience in data engineering / data platform rolesStrong hands-on experience with SQL (advanced), Spark / PySpark and Airflow (or similar)Experience building and debugging production-grade data pipelinesStrong understanding of Data modeling, ETL/ELT systems and Data quality challengesOwnership mindset, someone who fixes problems end-to-end

Why Join UsHigh ownership- you build, run, and improve what you shipOpportunity to shape data reliability and platform foundations