Data Engineer

Insight Global; Bengaluru Urban; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Insight Global is seeking a skilled Data Engineer to design, build, and optimize large-scale batch data pipelines in a cloud environment. The role emphasizes reliability, performance, and data quality to support analytics and downstream consumers.

Candidates should have strong experience with Spark, Hadoop, Hive, and SQL across BigQuery and Spark SQL, plus hands-on use of GCP services like BigQuery, Dataproc, Pub/Sub, and GCS. Familiarity with Airflow or Cloud Composer is a plus.

Qualifications

• Strong experience with Apache Spark, Hadoop, and Hive.
• Hands-on experience building batch data pipelines with a focus on performance, scalability, SLA adherence, and fault tolerance.
• Strong programming skills in Scala, with deep experience using Spark for data processing and analytics.
• Experience working with GCP services including BigQuery, Google Cloud Storage (GCS), Dataproc, and Pub/Sub.
• Solid experience writing and optimizing SQL, preferably BigQuery SQL and/or Spark SQL.
• Strong understanding of data modeling, ETL/ELT patterns, and data quality best practices.
• Experience with Kafka or similar messaging/streaming platforms.
• Familiarity with workflow orchestration tools (e.g., Airflow or Cloud Composer).
• Experience deploying and operating data pipelines in production cloud environments (GCP preferred, Azure acceptable).
• Strong troubleshooting skills and ability to optimize pipelines under real-world constraints.

Responsibilities

• Design, develop, and maintain batch data pipelines in a cloud environment using Spark, Hadoop, Hive.
• Build fault-tolerant, SLA-driven pipelines that scale reliably.
• Use GCP services (BigQuery, GCS, Dataproc, Pub/Sub) for ingestion and storage.
• Write and optimize SQL (BigQuery/Spark SQL) for analysis and performance.
• Collaborate with analytics, data science, and downstream teams to ensure data availability.
• Monitor pipelines, troubleshoot failures, and implement alerting and retries.
• Improve performance via partitioning, clustering, tuning, and query optimization.
• Follow software practices: version control, testing, and documentation.

Skills

Apache Spark

Hadoop

Hive

Scala

Spark SQL

SQL

Data modeling

ETL/ELT

Data quality

Troubleshooting

Kafka

Airflow

Cloud Composer

GCS

BigQuery

Dataproc

Pub/Sub

Production deployments

Tools

BigQuery

GCS

Dataproc

Pub/Sub

Kafka

Airflow

Cloud Composer

Job description

Strong experience with big data technologies such as Apache Spark, Hadoop, and Hive.

Hands-on experience building batch data pipelines with a focus on performance, scalability, SLA adherence, and fault tolerance.

Strong programming skills in Scala, with deep experience using Spark for data processing and analytics.

Experience working with GCP services including BigQuery, Google Cloud Storage (GCS), Dataproc, and Pub/Sub.

Solid experience writing and optimizing SQL, preferably BigQuery SQL and/or Spark SQL.

Strong understanding of data modeling, ETL/ELT patterns, and data quality best practices.

Experience with Kafka or similar messaging/streaming platforms.

Familiarity with workflow orchestration tools (e.g., Airflow or Cloud Composer).

Experience deploying and operating data pipelines in production cloud environments (GCP preferred, Azure acceptable).

Strong troubleshooting skills and ability to optimize pipelines under real-world constraints.

Job Description

We are seeking a skilled Data Engineer to design, build, and optimize large-scale batch data pipelines in a cloud environment. This role focuses on reliability, performance, and data quality, supporting analytics and downstream consumers through well-engineered big-data solutions. The ideal candidate has strong experience with Apache Spark, cloud data platforms (GCP preferred), and writing performant SQL at scale.

Key Responsibilities

• Design, develop, and maintain batch data pipelines using Apache Spark, Hadoop, Hive, or similar frameworks in a cloud environment.
• Build highly optimized, fault-tolerant, and SLA-driven data pipelines that operate reliably at scale.
• Leverage Google Cloud Platform (GCP) services such as BigQuery, GCS, Dataproc, and Pub/Sub to support data ingestion, processing, and storage.
• Write and optimize SQL queries (BigQuery SQL and/or Spark SQL) for data analysis, profiling, and performance tuning.
• Collaborate closely with analytics, data science, and downstream consumers to ensure data availability, correctness, and usability.
• Monitor and troubleshoot pipeline failures; implement alerting, retries, and data quality checks.
• Improve pipeline performance through partitioning, clustering, resource tuning, and query optimization.
• Follow software engineering best practices, including version control, testing, and documentation.