Hadoop Developer

Acestack Llc

Charlotte, NC, United States

about 2 months ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

5 years minimum

Working hours

Regular working hours

Job source

www.careerjet.com

Tech stack

Java (Programming Language) Agile Methodology Amazon Web Services Automation of Tests Microsoft Azure Unix Information Engineering Data Governance Extract Transform Load (ETL) Data Transformation Data Security Distributed Computing Environment

+25 more

Distributed Data Store Apache Hadoop Monitoring of Systems Apache Hive Python (Programming Language) Metadata Microsoft SQL Server Performance Tuning Software Engineering SQL Databases Teradata SQL Cloud Platform System Test-Driven Development (TDD) Data Ingestion Delivery Pipeline Snowflake Apache Spark Git Pyspark Data Lineage Deployment Automation Apache Kafka Software Version Control Data Pipelines Databricks

Requirements

Primary Skill: PySpark, Hive, Python, SQL, Hadoop Secondary: Unix, Agile, Base support Roles & Responsibilities

5+ years of experience in Data Engineering, Software Engineering, or related technical discipline.
Strong proficiency in Python, and SQL for advanced data transformations.
Hands-on experience designing and building ETL/ELT pipelines, data ingestion processes, and distributed data processing jobs.
Practical experience working with distributed data tools such as Apache Spark, Databricks, or Hadoop ecosystems.
Experience building and managing datasets in relational and/or cloud-based data platforms (Teradata, Snowflake, SQL Server, Azure/AWS/GCP).
Solid understanding of data modeling, metadata, data quality controls, data lineage, and secure data management.
Experience contributing to automated test suites, analyzing test failures, and supporting test-driven development.
Knowledge of CI/CD pipelines, version control (Git), and automated deployment practices.
Experience adhering to enterprise data governance, compliance, and operational risk frameworks.
Ability to troubleshoot pipeline issues, performance bottlenecks, and data discrepancies.
Strong communication skills and ability to collaborate across engineering, product, and business teams.
Experience implementing monitoring and observability for data pipelines (logs, metrics, health checks).
Advanced experience with performance tuning of SQL, Spark, or distributed data workflows.
Knowledge of data security practices (encryption, masking, PII handling).
Experience supporting analytical workloads, BI tools, or data science teams.
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