Full Time
San Francisco
Posted 5 days ago
0.000000 - 0.000000

HMG AMERICA LLC

Overview

A Spark job migration specialist migrates data pipelines, JAR tasks, and analytics workloads from legacy systems (like Hadoop/CDH or AWS EMR) to ACOS modern platforms. This involves refactoring code (e.g., Hive to PySpark), performance testing, and updating Spark 2.x to 3.x.

About the Role

Responsibilities

Workload Migration: Migrate JVM workloads and Spark-Submit tasks to Databricks JAR tasks or Notebook tasks.
Pipeline Re-engineering: Convert existing HiveQL scripts and Oozie workflows into optimized Spark SQL or PySpark applications.
Refactoring: Adapt data pipelines from Azure Synapse to any cloud platform, including updating library dependencies and notebook references.
Performance Optimization: Implement Adaptive Query Execution (AQE) in Spark 3 to improve shuffle performance and fix skew joins.
Testing & Validation: Perform regression testing to ensure output consistency between old and new systems using validation scripts.
Job Customization: Use spark.sparkContext.setJobDescription() to label, monitor, and troubleshoot specific Spark tasks in the UI.

Qualifications

Experience: 5+ years experience with Apache Spark (PySpark/Scala) and Cloud platforms (Azure/AWS).

Required Skills

Strong experience with HDFS, Hadoop ecosystem (Hive, Spark, HBase, MapReduce).
Experience in data migration to cloud / enterprise data platforms.
Knowledge of:
Cloud storage (ADLS, S3, Blob Storage)
Distributed processing frameworks
SQL and performance tuning expertise.
Experience in scripting (Python, Shell, Scala).

Preferred Skills

Data Pipelines: Ensuring schema evolution, data correctness, and testing with golden datasets.
Job Definitions: Reconfiguring job properties, cluster settings, and Spark configurations.

Pay range and compensation package

Pay range or salary or compensation details not provided.

Equal Opportunity Statement

We are committed to diversity and inclusivity.

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