Data Engineer

Role Overview: As a Data Engineer at WebEngage, you will be responsible for owning the end-to-end lifecycle of data, from ingesting raw event streams to building reliable datasets that power analytics, predictive models, and customer journey orchestration. This role requires a high level of impact and hands-on work at the intersection of product, analytics, and backend engineering. You will need to focus on designing for data quality, pipeline observability, cost efficiency, and long-term maintainability from day one. Key Responsibilities: - Design, build, and maintain production-grade ETL/ELT pipelines that ingest data from various sources into the central data warehouse. - Implement idempotent, incremental load patterns with retry logic and SLA-based alerting for zero-data-loss pipelines. - Ensure pipeline observability by setting up data freshness checks, schema drift detection, and anomaly alerts. - Translate business requirements into clean dimensional models and maintain a well-documented data catalogue. - Write clean, modular, well-tested Python and SQL code following best practices and participate in peer code reviews. - Build interactive dashboards and analytical tools for stakeholders to explore metrics and make data-driven decisions. - Partner with product managers, analysts, and data scientists to understand data needs and document data lineage and transformation logic. Qualifications Required: - Strong SQL skills with expertise in complex queries and performance optimization on cloud data warehouses. - Proficiency in Python scripting for data ingestion, transformation, and validation. - Experience with end-to-end ownership of data pipelines, data modelling, and translating business needs into optimized table structures. - Bachelors degree in Computer Science, Engineering, Mathematics, Statistics, or related quantitative field. - 13 years of professional experience in data engineering or analytics engineering. - Understanding of data warehouse architecture and familiarity with version control workflows and agile development practices. (Note: Any additional details of the company were not mentioned in the provided job description.) Role Overview: As a Data Engineer at WebEngage, you will be responsible for owning the end-to-end lifecycle of data, from ingesting raw event streams to building reliable datasets that power analytics, predictive models, and customer journey orchestration. This role requires a high level of impact and hands-on work at the intersection of product, analytics, and backend engineering. You will need to focus on designing for data quality, pipeline observability, cost efficiency, and long-term maintainability from day one. Key Responsibilities: - Design, build, and maintain production-grade ETL/ELT pipelines that ingest data from various sources into the central data warehouse. - Implement idempotent, incremental load patterns with retry logic and SLA-based alerting for zero-data-loss pipelines. - Ensure pipeline observability by setting up data freshness checks, schema drift detection, and anomaly alerts. - Translate business requirements into clean dimensional models and maintain a well-documented data catalogue. - Write clean, modular, well-tested Python and SQL code following best practices and participate in peer code reviews. - Build interactive dashboards and analytical tools for stakeholders to explore metrics and make data-driven decisions. - Partner with product managers, analysts, and data scientists to understand data needs and document data lineage and transformation logic. Qualifications Required: - Strong SQL skills with expertise in complex queries and performance optimization on cloud data warehouses. - Proficiency in Python scripting for data ingestion, transformation, and validation. - Experience with end-to-end ownership of data pipelines, data modelling, and translating business needs into optimized table structures. - Bachelors degree in Computer Science, Engineering, Mathematics, Statistics, or related quantitative field. - 13 years of professional experience in data engineering or analytics engineering. - Understanding of data warehouse architecture and familiarity with version control workflows and agile development practices. (Note: Any additional details of the company were not mentioned in the provided job description.)