Lead Data & Analytics Engineer - PySpark (India)

Lead Data & Analytics Engineer - PySpark (India)

02 Oct
|
Java R u0026 D
|
India

02 Oct

Java R u0026 D

India

Designation: Lead Data and Analytics Engineer

Position Overview:

We are seeking a hands-on Senior Data and Analytics skilled who will design, build, and operate the enterprise data and analytics ecosystem covering:

- Data Engineering and Data Platforms - open-source lakehouse stack (primary), with Fabric / Snowflake / Databricks as additional platforms
- Business Intelligence (BI) and Analytics delivery
- AI-ready data preparation and modeling
- Embedded and platform-native AI capabilities within analytics tools

This role is focused on doing rather than managing - the individual will personally build pipelines, write transformation code, model data, create semantic layers, and develop BI dashboards. The platform foundation is open-source first: the candidate must be comfortable building and maintaining open-source data infrastructure (Apache Spark, Delta Lake, dbt, Airflow, Trino/Presto, Great Expectations and similar).

Experience with managed platforms (Microsoft Fabric, Snowflake, Databricks) is a strong add-on but secondary to open-source depth.

Required Skills and Competencies:

Technical: Must-Have:

- Open-source data engineering stack (hands-on, production-grade):

1.

Apache

Spark / PySpark - personally wrote and optimised Spark jobs in production

2.

Delta





Lake / Iceberg / Hudi - built and operated medallion-architecture lakes

- dbt - authored transformation models, tests and documentation

4.

Apache

Airflow (or Prefect / Dagster) - built and maintained DAGs in production

- Docker - containerised data platform components; basic Kubernetes familiarity
- CI/CD for data pipelines - GitHub Actions / GitLab CI
- Strong SQL - complex transformations, window functions, query optimisation.
- Data modelling - dimensional modelling (star/snowflake schemas), semantic layer design.
- Power BI - hands-on dashboard and semantic model development.
- End-to-end ownership - personally built and maintained pipelines and BI artefacts in production, not just designed or reviewed them.

Technical - Strong Add-on:

- Microsoft Fabric - Lakehouse, Delta tables, Direct Lake, Fabric Copilot
- Snowflake - data loading, transformation, performance tuning
- Azure Databricks - Delta Live Tables, Unity Catalog, Workflows
- Apache Kafka / Flink - streaming ingestion
- Trino / Presto - federated query
- Great Expectations / Soda - open-source data quality frameworks
- OpenMetadata / DataHub / Apache Atlas - data cataloguing and lineage

📌 Lead Data & Analytics Engineer - PySpark (India)
🏢 Java R u0026 D
📍 India

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