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Senior Data Engineer

Senior Data Engineer

Delhivery

0 years

0 Lacs

noida uttar pradesh india

Posted:2 months ago| Platform:

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Skills Required

data logistics network transportation cutting technology transactions design processing ai tooling workflow debugging testing documentation spark kafka hive databricks power analytics learning apache versioning aws model pipeline joins code refactoring stack sql development automation airflow hadoop mapreduce tuning nosql dynamodb programming python scala java git jenkins github engineering efficiency

Work Mode

On-site

Job Type

Full Time

Job Description

About Delhivery:

Delhivery is India’s leading fulfillment platform for digital commerce. With a vast logistics network spanning 18,000+ pin codes and over 2,500 cities, Delhivery provides a comprehensive suite of services including express parcel transportation, freight solutions, reverse logistics, cross-border commerce, warehousing, and cutting-edge technology services. Since 2011, we’ve fulfilled over 550 million transactions and empowered 10,000+ businesses, from startups to large enterprises.

Vision

.

About the Role: Senior Data Enginee

a Senior Data Engine

What You’ll Do

Build and optimize scalable batch and streaming data pipelines using Apache Spark, Kafka, Flink, Hive, and Airflow.
Design and implement efficient data lake architectures with Hudi, Iceberg, or Delta for versioning, compaction, schema evolution, and time travel.
Architect and maintain cloud-native data systems (AWS EMR, S3, Glue, Lambda, Athena), focusing on cost, performance, and availability.
Model complex analytical and operational data workflows for warehouse and data lake environments.
Own pipeline observability — define and monitor SLAs, alerts, and lineage across batch and real-time systems.
Debug performance bottlenecks across Spark, Hive, Kafka, and S3 — optimizing jobs with broadcast joins, file formats, resource configs, and partitioning strategies.
Leverage AI tools (e.g., Cursor AI, Copilot, Gemini, Windsurf) for Code generation and refactoring of DAGs or Spark jobs
Debugging logs, stack traces, and SQL errors. Generating tests for data pipelines
Documenting complex pipeline dependencies and architecture
Collaborate with product, analytics, data science, and platform teams to deliver end-to-end data products.
Mentor junior engineers and establish AI-native development workflows, including prompt libraries and automation best practices.

What We’re Looking For:

Experience in building and maintaining large-scale data systems.
Strong hands-on experience with Apache Spark, Kafka, Hive, and Airflow in production.
Deep knowledge of the Hadoop ecosystem (HDFS, YARN, MapReduce tuning, NameNodeHA).
Expert in SQL (windowing, recursive queries, tuning) and experience with NoSQL stores (e.g., DynamoDB, HBase).
Experience with trino/prestoExperience with cloud-native data platforms — especially AWS Glue, S3 lifecycle policies, EMR, and Athena.
Working knowledge of file formats and internals like Parquet, Avro, and best practices for efficient storage.
Familiarity with modern Lakehouse formats (Hudi, Iceberg, Delta Lake) and their compaction, versioning, and schema evolution.
Hands-on experience managing Databricks or EMR.
Solid grounding in data modeling, DWH design, and slowly changing dimensions (SCD).
Strong programming in Python/Scala/Java, and ability to write clean, modular, testable code.
Proficiency with CI/CD practices, Git, Jenkins/GitHub Actions for data engineering workflows.
Bonus: Experience with distributed systems, consensus protocols, and real-time data guarantees.
Passion for AI-native engineering — using and evolving prompt-based workflows for greater efficiency and quality.

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Senior Data Engineer