We are looking for a hands-on Data Engineer to help build and manage our data platform for reporting, analytics, and future data science use cases.

The role will involve working with SQL, Python, PySpark, AWS, ETL/ELT pipelines, data warehouses, and BI/reporting tools. Our architecture may use technologies such as ClickHouse, Redshift, AWS Glue, Airflow, Step Functions, Lambda, S3, and CDC-based replication tools based on scale, cost, and operational needs.

Key Responsibilities

- Design, build, and maintain scalable ETL/ELT data pipelines from multiple databases, applications, and external systems.
- Build raw, cleaned, and business-ready data layers to support reporting, analytics, and future data science use cases.
- Write efficient SQL, Python, and PySpark jobs for data ingestion, transformation, validation, and processing.
- Implement workflow orchestration using Apache Airflow, AWS Glue, Step Functions, or similar tools.
- Work with data warehouses such as ClickHouse, Redshift, Snowflake, BigQuery, or similar.
- Support cross-service reporting as the architecture moves towards independent microservice databases.
- Build reusable reporting tables, aggregates, summaries, and basic data marts.
- Monitor, troubleshoot, and optimize data pipelines, warehouse queries, and processing jobs.
- Implement data quality checks for freshness, completeness, consistency, duplicates, and reconciliation.




- Support BI/reporting needs through tools such as Power BI, Metabase, Superset, Redash, or similar.
- Apply data governance, access control, security, and PII-handling best practices.
- Collaborate with engineering, DevOps, product, business, finance, risk, and support teams.

Required Skills

- Robust expertise in SQL for joins, aggregations, window functions, query optimization, and analytical reporting.
- Hands-on experience with Python for data processing, automation, validation, and scripting.
- Working experience with PySpark / Apache Spark for processing large datasets.
- Good understanding of ETL/ELT pipelines, data warehousing, and data modelling concepts.
- Experience with workflow orchestration using Apache Airflow, AWS Step Functions, AWS Glue, or similar tools.
- Experience with AWS data services such as S3, Glue, Lambda, Step Functions, Redshift, DMS, CloudWatch, or similar.
- Experience with any data warehouse such as ClickHouse, Redshift, Snowflake, BigQuery, or similar.
- Understanding of relational databases, preferably PostgreSQL.
- Ability to debug data mismatches, failed pipelines, slow queries, and data quality issues.
- Exposure to BI tools such as Power BI, Metabase, Superset, Redash, or similar.

Good ownership, problem-solving, communication, and collaboration skills. Skills:- Data engineering, ClickHouse, Amazon Web Services (AWS) and PySpark

📌 Data Engineer (Mumbai)
🏢 novio
📍 Mumbai