Key Responsibilities

ETL Design and Development

- Design, implement, and maintain ETL processes to extract, transform, and load data from diverse sources into data warehouses or other storage systems.
- Optimize ETL workflows for performance, reliability, and scalability.
- Build reusable ETL components and enforce data quality and observability across pipelines.

Data Integration

- Develop and manage data integration solutions, integrating structured and unstructured data from multiple sources.
- Work with APIs, databases, and flat files to retrieve, process, and stage data.
- Ensure data lineage, cataloging, and metadata are properly maintained.

Data Transformation

- Clean, transform, and standardize data to meet business and analytics requirements.
- Apply validation, enrichment, and aggregation techniques to produce reliable datasets.
- Implement error handling and data reconciliation mechanisms.

Data Warehouse Development

- Support the design and development of schemas (star/snowflake) and dimensional models.
- Implement and manage database objects such as tables, views, stored procedures, indexes, and partitions.
- Collaborate with BI/Analytics teams to ensure models meet reporting and performance needs.

Monitoring and Troubleshooting

- Monitor ETL processes for failures, inconsistencies, and SLA breaches; troubleshoot issues proactively.
- Implement alerting, retry, and recovery mechanisms; maintain runbooks and SOPs.
- Track pipeline health via dashboards and logs; conduct root cause analysis (RCA) and performance tuning.

Mandatory Skills

- ETL Tools/Frameworks: Hands-on with at least one (e.g., Talend, Informatica, Pentaho, SSIS, dbt, Apache Airflow/Luigi/NiFi).
- SQL &
- Data Modeling: Strong SQL, dimensional modeling (star/snowflake),



query optimization.
- RDBMS: Experience with Oracle and PostgreSQL (DDL/DML, indexing, partitioning, performance tuning).
- Kafka: Practical experience with topics, producers/consumers, offsets, schema management, and cluster monitoring.
- Scripting: Python or Shell for automation, data parsing, and ETL orchestration.
- APIs &
- Files: Integration using REST APIs, JSON/CSV/Parquet, and file-based ingestion patterns.
- Monitoring &
- Observability: Experience with pipeline monitoring, logging, and alerting (e.g., Grafana/Prometheus/ELK).
- Version Control &
- CI/CD: Git and CI/CD for ETL deployments.

Desirable Skills

- Cloud Data Platforms: AWS/Azure/GCP (e.g., S3/ADLS, Glue/Data Factory, Lambda/Functions).
- Streaming &
- Batch: Experience with Kafka Connect, ksqlDB, or Spark/Flink for streaming ETL.
- Orchestration: Airflow DAG design best practices
- SLA management and backfills.
- Data Quality: Outstanding Expectations / Deequ; data validation frameworks.
- Performance Tuning: SQL and pipeline performance tuning at scale.
- Security &
- Compliance: Row/column-level security, encryption at rest/in transit, data governance.

Behavioral &

- Professional Attributes

- Strong analytical and problem-solving abilities with a data-driven mindset.
- Ownership-oriented; able to work independently and in cross-functional teams.
- Clear and concise communication with stakeholders (engineering, BI, product).
- Detail-focused with strong documentation habits (runbooks, SOPs, design specs).
- Comfortable working in fast-paced environments with shifting priorities and SLAs.

Nice-to-Have (Tools &

- Ecosystem)

- Experience with dbt, Snowflake/Redshift/BigQuery, Oracle GoldenGate, or AWS DMS.
- Exposure to data catalog tools and metadata management.

📌 Reporting and Data Migration lead (Bengaluru)
🏢 Comviva
📍 Bengaluru