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Bigdata Engineer -Spark

Marktine Technology Solutions Pvt Ltd · United States · back to the listing

HR standards

43/100

poor

Title ↔ description

76/100

solid

Reads as

Data Engineer

100% confident

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What the listing never says

  • 17 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
  • No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
  • No location or timezone policy stated, so a candidate cannot tell where they may work from. Scope clarity

The listing, marked up

Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.

Category: Technology

Location:

Responsibilities:

• As a developer, possess excellent Knowledge of distributed computing architecture, core hadoop component (HDFS, Spark, Yarn, Map-Reduce, H base, HIVE, Impala) and related technologies.

• Technical Design and development of ETL/Hadoop and Analytics services /components

• Contribute in end to end architecture and process flow

• Understand Business requirement and publish reusable designs

• Result oriented approach with ability to provide apt solutions.

• Proficient in performance improvement & fine-tuning ETL and Hadoop implementations

• Conduct code reviews across projects. Takes responsibility for ensuring that build and code adhere to architectural and quality standards and policies.

• Can work independently with minimum supervision.

• Strong analytical and problem solving skills

• Experience/Exposure to SQL, advanced SQL skills

Requirements

Skills Set:

• Strong understanding of distributed computing architecture, core hadoop component (HDFS, Spark, Yarn, Map-Reuduce, H base, HIVE, Impala) and related technologies.

• Hands on experience with batch data ingestion (Sqoop)

• Expert level understanding of relational data structure and RDBMS as well as NoSQL databases (Cassandra, MongoDB, Elasticsearch)

• Experience with automation/Scheduling of workflows/jobs (via shell-scripting, Tivoli)

• Solid Grasp of data storage formats (Parquet, Avro, HBase, Cassandra)

• Understanding of Agile methodologies as well as SDLC life-cycles and processes.

• Strong Understanding of Data warehousing and lakes

DetailsOriginally posted on Himalayas