About the Role

We are looking for a Big Data Engineer to design, build, and operate large-scale data pipelines and analytical infrastructure that transform high-volume raw data into reliable, query-ready datasets for analytics, reporting, and data-driven products.

Our data platform ingests and processes data from multiple sources and serves analytics, data science, product, and downstream applications. In this role, you will own data pipelines end-to-end-from ingestion and transformation to warehousing, orchestration, data quality, and observability.

A key part of the role is owning ClickHouse as our primary analytical data store . You will be responsible for designing scalable data models, optimizing query performance, and ensuring the platform remains reliable and cost-efficient as data volumes and workloads grow.

You will work closely with data scientists, analysts, product engineers, and other engineering teams to build a up-to-date, cloud-native data platform.

What Youll Do
- Design, build,



and maintain robust batch and streaming data pipelines that ingest data from multiple sources into analytical data stores.
- Build and operate Apache Airflow DAGs , including scheduling, dependencies, retries, backfills, idempotency, concurrency, and failure handling.
- Develop analytics-ready datasets using dbt , following well-structured staging, intermediate, and mart layers with appropriate tests, documentation, and incremental models.
- Own ClickHouse as the primary analytical store, including:
- Schema and table design using the MergeTree family of engines
- Partitioning and sorting/primary key strategies
- Materialized views
- Distributed and replicated table architectures
- Query and memory optimization
- High-volume data ingestion and performance tuning
- Work with BigQuery where cloud data-warehouse patterns are appropriate, including data modeling and query/cost optimization.
- Design and operate NoSQL and key-value data stores , including Bigtable, DynamoDB, and

📌 Big Data Engineer (India)
🏢 Wingify Software
📍 India