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2d ago

Sr Hadoop+Spark(scala) Data Engineer

Virtues Irving, TX

Irving, TX

2d ago

$101k - $110k/yr Full time Senior Big Data Bigdata

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Job Description

Virtues is offering a senior Big Data engineering role focused on Hadoop, Spark, and Scala, with an onsite presence in Irving, TX. The position provides a competitive annual salary ranging from USD 100,696 to 110,268, and an opportunity to design, develop, and support scalable batch and real-time data pipelines across multiple data platforms.

Responsibilities

Design, develop, implement, and maintain scalable, high-performance data ingestion and processing pipelines using Hadoop ecosystem technologies.
Develop and manage data pipelines supporting batch processing, real-time/event-driven processing, and streaming data workflows.
Read data from structured, semi-structured, and unstructured sources.
Ingest batch data and real-time event streams, including Kafka events.
Perform complex data validation, cleansing, enrichment, and transformation.
Deliver processed data to target data stores, curated layers, publishing zones, and downstream endpoints.
Develop and optimize Apache Spark applications using Scala for large-scale distributed processing.
Design and implement Kafka-centric event processing and real-time data pipelines.
Develop streaming data transformation logic using Apache Spark Streaming and/or Spark Structured Streaming.
Build and maintain scalable batch processing solutions using Apache Spark.
Develop data processing and analytical solutions using HiveQL, Pig Latin, HBase, and custom MapReduce programs.
Develop data transformation and integration processes to move data from raw zones to curated and published data warehouse layers.
Collaborate with data architects, application teams, business stakeholders, and platform teams to translate requirements into scalable technical solutions.
Work extensively with Hadoop ecosystem technologies including HDFS, MapReduce, Hive, Pig, Sqoop, HBase, ZooKeeper, Oozie, Apache Spark, Scala, Flume/Flume NG, Kafka, and Hue.
Apply strong knowledge of Hadoop architecture and core components such as NameNode, DataNode, HDFS, JobTracker, TaskTracker, and MapReduce programming/execution.
Install, configure, integrate, and support Hadoop ecosystem components within Cloudera-based environments.
Work with distributed storage and processing frameworks to ensure scalability, reliability, fault tolerance, and high performance.
Monitor and optimize data pipeline performance, resource utilization, throughput, and processing efficiency.

Requirements

Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, Data Science, or a related technical discipline.
7+ years of hands-on experience in the Hadoop framework and the broader Hadoop ecosystem.
6+ years of hands-on experience developing data ingestion and integration solutions across multiple data platforms.
5+ years of strong hands-on experience with Apache Spark using Scala for distributed data processing.
5+ years of experience in data modeling, data transformation, detailed technical design, and data integration.
Proven experience designing and developing large-scale batch and real-time data pipelines.
Strong experience with HiveQL, Pig Latin, HBase, and custom MapReduce programming.
Experience developing and managing Kafka-centric event-driven data pipelines.
Solid understanding of batch processing, stream processing, and event-driven architectures.
Hands-on experience with Spark Streaming and/or Spark Structured Streaming.
Experience installing and configuring Cloudera Hadoop ecosystem components, including Hive, HBase, ZooKeeper, Oozie, Spark, Sqoop, Flume, Pig, and Hue.
Strong understanding of Hadoop architecture, HDFS, distributed storage, and MapReduce concepts.
Strong analytical, problem-solving, debugging, and performance-tuning skills.
Excellent communication and collaboration skills.

Technologies

Hadoop, HDFS, MapReduce, Hive, Pig, Sqoop, HBase, ZooKeeper, Oozie
Apache Spark, Spark Streaming, Spark Structured Streaming
Scala, Flume/Flume NG, Kafka, Hue
HiveQL, Pig Latin
BigQuery, Cloudera, MapR, Hortonworks

Key Competencies

Strong expertise in distributed data processing and big data architecture
Deep understanding of batch, real-time, streaming, and event-driven processing
Proficiency in Scala and distributed data engineering frameworks
Ability to design scalable, fault-tolerant, high-performance data solutions
Analytical problem-solving, debugging, and root-cause analysis capabilities
Ability to work independently and collaborate with cross-functional teams
Ownership, attention to detail, and commitment to data quality and operational excellence

Desirable and Nice-to-have Skills

End-to-end Hadoop administration and production support
Hadoop infrastructure setup, installation, configuration, upgrades, patching, monitoring, troubleshooting, and maintenance
Administration of Hadoop distributions such as Cloudera, MapR, Hortonworks
Installing, configuring, and managing ecosystem components: Hive, Pig, HBase, ZooKeeper, Oozie, Spark, Sqoop, Flume, Hue
Managing and monitoring HDFS, distributed file systems, and Hadoop clusters
Managing, monitoring, scheduling, and troubleshooting MapReduce and distributed processing jobs
Cluster capacity planning, resource management, health monitoring, and operational support
Automation of operational activities via scripting (backup/restore, monitoring, health checks, maintenance, reporting)
Experience with version control, change management, release management, incident management, problem management, and root-cause analysis
Nice-to-have: Hadoop Platform Administration, GCP BigQuery

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