ETL Developer

Primary Title:

About The Opportunity

Role & Responsibilities

• Design, build and maintain scalable ETL/ELT pipelines to ingest, transform and load data from multiple source systems into the central data warehouse.
• Implement robust data transformations using SQL, Python/Scala and ETL tools (Informatica/SSIS/Talend/Azure Data Factory/AWS Glue) with clear lineage and documentation.
• Develop and operate workflow orchestration (Airflow or native cloud schedulers), ensuring reliable scheduling, retry logic and alerting for production jobs.
• Optimize query and pipeline performance: partitioning, indexing, Spark tuning, and cost-efficient cloud resource usage.
• Implement data quality checks, monitoring, logging and automated alerts to maintain pipeline reliability and SLA adherence.
• Collaborate with analysts, data scientists and product teams to translate business requirements into performant data models and documentation; support release & CI/CD processes.
  

Skills & Qualifications

• Must-Have: 3+ years hands-on ETL development experience; advanced SQL skills and production experience with at least one ETL tool (Informatica, SSIS, Talend, ADF or AWS Glue).
• Must-Have: Strong scripting skills in Python or Scala for data transformation, automation and troubleshooting.
• Must-Have: Practical experience with cloud data warehouses (Snowflake, Redshift, BigQuery) and knowledge of data modelling (star/snowflake schemas).
• Must-Have: Experience operating workflows/orchestrators (Airflow) and applying data quality, monitoring and alerting patterns in production.
• Preferred: Familiarity with Spark (batch/streaming), CI/CD for data pipelines, Git, and container-based deployment.
• Preferred: Exposure to large-scale data ingestion patterns, cost-optimisation in cloud environments and prior remote/ distributed team experience.
  

Benefits & Culture Highlights

• Fully remote role across India with flexible hours and a focus on outcomes over face-time.
• Opportunity to work on cloud-first data platforms, modern ETL tooling and influence data architecture decisions.
• Learning allowance, technical training and clear career progression paths within a fast-scaling product and engineering organisation.