Big Data Engineer
Description
Job Title: Big Data Engineer
Location: Atlanta, GA / Tampa, FL/ Dallas, TX
Job Summary
We are seeking an experienced Big Data Engineer to design, build, and optimize large-scale data pipelines and distributed systems across cloud and on-prem platforms. The ideal candidate will have strong expertise in Spark, Hadoop ecosystems, cloud data services, ETL/ELT design, streaming platforms, and best practices for scalable data processing.
Responsibilities
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Design, develop, and maintain big data pipelines using Spark (PySpark/Scala), Hadoop, Kafka, and distributed computing frameworks.
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Build and optimize ETL/ELT pipelines for structured and unstructured data across cloud and on-prem data platforms.
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Work with cloud technologies (AWS, Azure, or GCP) including Data Lake, Databricks, EMR, Glue, Dataflow, Synapse, or Snowflake.
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Develop Delta Lake / Lakehouse architecture for high-performance ingestion and processing.
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Build real-time streaming solutions using Kafka, Spark Streaming, Kinesis, or Event Hub.
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Collaborate with data architects, analysts, and application teams to gather data requirements and deliver scalable solutions.
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Implement and maintain CI/CD pipelines, automated jobs, and orchestration using Airflow/Azure Data Factory/Glue Workflows.
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Optimize data pipelines for performance, cost efficiency, and reliability.
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Ensure data quality, validation, governance, and lineage best practices across the ecosystem.
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Troubleshoot and resolve production issues in a high-availability environment.
Required Skills & Qualifications
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5–8+ years of experience as a Big Data Engineer or Data Engineer.
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Strong hands-on skills with Spark (PySpark or Scala), Hadoop, Hive, HDFS, MapReduce.
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Experience working with Databricks, EMR, Glue, Synapse, DataProc, or equivalent big-data compute engines.
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Proficiency in Python or Scala for data engineering.
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Experience with Kafka or other event-streaming technologies.
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Strong understanding of cloud data architectures (AWS S3/Glue/EMR | Azure ADLS/ADF/Databricks | GCP BigQuery/DataProc).
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Solid SQL skills and experience with relational + NoSQL databases.
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Experience with version control (Git) and CI/CD tools (Jenkins, Azure DevOps, GitHub Actions).
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Hands-on experience with Airflow, ADF, or other orchestration and scheduling tools.
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Familiarity with data modeling, data governance, and best practices for data quality.
Skills
- Airflow
- AWS
- Azure
- Azure Data Factory
- Azure DevOps
- Big Data
- BigQuery
- CI/CD
- Cloud
- Data Engineering
- Data Governance
- Data Lake
- Data Modeling
- Data Pipelines
- Data Quality
- Databricks
- Dataproc
- Delta Lake
- DevOps
- Distributed Computing
- Distributed Systems
- ELT
- ETL
- GCP
- Git
- GitHub
- GitHub Actions
- Hadoop
- Hive
- Jenkins
- Kafka
- Kinesis
- Lakehouse
- NoSQL
- PySpark
- Python
- S3
- Scala
- Snowflake
- Spark
- SQL
- Version Control