Design, build, and maintain large-scale data pipelines and ETL processes using PySpark/Apache Spark, SQL/NoSQL stores, Hadoop, and Kafka. Work with cloud platforms (AWS/Azure/GCP), data lakes and warehouses, and Python for data manipulation. Lead projects, perform systems analysis, ensure delivery under deadlines, and collaborate across teams. CI/CD and containerization experience is a plus.
Requirements and Qualifications:
- 8+ years of relevant experience in the Data Engineering, ApacheSpark
- Strong proficiency in PySpark and Apache Spark.
- Solid experience with SQL, NoSQL databases (e.g., Hive, HBase, Cassandra).
- Hands-on experience with big data frameworks (Hadoop, Kafka, etc.).
- Expertise in cloud platforms (AWS, Azure, or GCP).
- Proficiency in Python programming and data manipulation.
- Knowledge of ETL tools, data lakes, and data warehouses.
- Experience in CI/CD, containerization (Docker, Kubernetes) is a plus.
- Strong problem-solving and analytical skills.
- Excellent communication and teamwork skills.
- Experience in systems analysis and programming of software applications
- Experience in managing and implementing successful projects
- Ability to work under pressure and manage deadlines or unexpected changes in expectations or requirements
Compensation, Benefits and Duration
Minimum Compensation: USD 46,000
Maximum Compensation: USD 161,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees.
This position is not available for independent contractors
No applications will be considered if received more than 120 days after the date of this post
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