# Data Engineer

> Weekday AI · Mumbai, India · Full-time · Posted 2026-09-17

**Salary:** INR 1,800,000–2,300,000

**Workplace:** on_site

**Department:** Weekday's Client via platform

## Description

𝗧𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗳𝗼𝗿 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸𝗱𝗮𝘆'𝘀 𝗰𝗹𝗶𝗲𝗻𝘁𝘀

𝗦𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗻𝗴𝗲: 𝗥𝘀 𝟭𝟴𝟬𝟬𝟬𝟬𝟬 - 𝗥𝘀 𝟮𝟯𝟬𝟬𝟬𝟬𝟬 (𝗶𝗲 𝗜𝗡𝗥 𝟭𝟴-𝟮𝟯 𝗟𝗣𝗔)

Experience: 3+ yrs

Location: Mumbai, Maharashtra, India

Job Type: Full-time

We are looking for an experienced **Data Engineer** to design, develop, and maintain scalable data platforms and pipelines using **AWS, Apache Spark, Python, SQL, Kafka, and modern data engineering frameworks**.

The role will focus on building reliable data solutions for high-volume batch and real-time workloads, including data ingestion, transformation, processing, orchestration, storage, and delivery. The ideal candidate will have strong hands-on experience with AWS data services and distributed data processing, along with a solid understanding of data modelling, streaming architectures, data quality, and pipeline optimisation.

## Requirements

KEY RESPONSIBILITIES

-   Design, develop, and maintain end-to-end **data pipelines** for high-volume data ingestion, transformation, processing, and delivery.
-   Build scalable **Spark-based ETL/ELT workflows** for both batch and real-time data processing.
-   Develop data ingestion solutions using **Kafka, Amazon Kinesis**, and other streaming technologies.
-   Build and manage data lakes, warehouses, and lakehouse solutions using **AWS S3, Glue, Redshift, Athena, and EMR**.
-   Develop efficient data models using dimensional modelling, star schemas, partitioning, and other data engineering practices.
-   Implement data quality checks, validation rules, monitoring, and error-handling mechanisms.
-   Develop automated workflows using **Airflow, MWAA, AWS Step Functions**, or similar orchestration tools.
-   Collaborate with Data Analysts and Data Scientists to deliver clean, structured, and analytics-ready datasets.
-   Optimise data pipelines for **performance, scalability, reliability, and AWS cost efficiency**.
-   Integrate data from multiple internal and external systems while maintaining data consistency and reliability.
-   Develop Python-based automation and data-processing solutions.
-   Monitor production pipelines, troubleshoot failures, and perform root-cause analysis.
-   Follow modern software engineering practices including **Git, CI/CD, testing, documentation, and code reviews**.
-   Contribute to data platform architecture, engineering standards, and continuous improvement initiatives.
-   Support data governance, cataloguing, lineage, and metadata management practices where required.
-   Work with Linux/Unix environments and efficiently process large datasets.

WHAT MAKES YOU A GREAT FIT

-   **3+ years of professional experience** in Data Engineering, Big Data, Analytics Engineering, or a related field.
-   Strong hands-on programming experience with **Python** for data processing, automation, and pipeline development.
-   Strong expertise in **Apache Spark**, particularly PySpark and/or Spark SQL.
-   Deep working knowledge of the **AWS data ecosystem**, including S3, Glue, Redshift, Athena, EMR, Kinesis, Lambda, and IAM.
-   Hands-on experience with **Kafka, Kinesis, Flink**, or similar real-time streaming technologies.
-   Strong command of **SQL** and experience with data modelling, dimensional modelling, partitioning, and large-scale data processing.
-   Experience with **Airflow, MWAA, Step Functions**, or comparable workflow orchestration tools.
-   Strong understanding of batch and real-time data processing architectures.
-   Experience working with high-volume datasets and distributed data processing environments.
-   Familiarity with **Git, CI/CD, testing, and modern software development practices**.
-   Comfortable working in **Linux/Unix environments**.
-   Strong troubleshooting, analytical, and problem-solving skills.
-   Experience with **Delta Lake, Apache Iceberg, Hudi**, or other lakehouse technologies is an advantage.
-   Knowledge of data governance, cataloguing, metadata, and lineage tools such as **Glue Data Catalog, DataHub, or Amundsen** is a plus.
-   Familiarity with **Docker, ECS, or EKS** and containerised deployments is desirable.
-   Basic understanding of **AI/ML data requirements and workflows** is an advantage.
-   Bachelor's degree in **Computer Science, Information Technology, Engineering, or a related technical discipline**is preferred.

## Apply

[Apply at Weekday AI](https://apply.workable.com/weekday-1/j/1E7F614D53/apply)

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