Data Engineer

4 days ago

India R Systems Full time
Job Title:

Data Engineer
Contract Period:

12 Months
Location:

Offshore candidates accepted (Singapore Based Company)
Work Timing : 6.30 AM to 3.30 PM or 7.00 AM to 4.00 PM (IST - India timing)

Experience
Minimum

4+ years

as a

Data Engineer

or similar role.(Please don't apply if less than 4 years exp in Data Engineer)
Proven experience in

Python, Spark, and PySpark

(non-negotiable).(mandatory*)
Hands-on in building

ETL pipelines, real-time streaming, and data transformations .
Worked with

data warehouses, cloud platforms (AWS/Azure/GCP) , and

databases .

Technical Skills
Spark Core API : RDDs, transformations/actions, DAG execution.
Spark SQL : DataFrames, schema optimization, UDFs.
Streaming : Structured Streaming, Kafka integration.
Data Handling : S3, HDFS, JDBC, Parquet, Avro, ORC.
Orchestration : Airflow/Prefect.
Performance Tuning : Partitioning, caching, broadcast joins.
Cloud Deployment : Databricks, AWS EMR, Azure HDInsight, GCP Dataproc.
CI/CD : Pytest/unittest for Spark jobs, Jenkins, GitHub Actions.

Education & Soft Skills
Bachelor's/Master's in Computer Science, Computer Engineering, or equivalent.
Strong analytical, problem-solving, and communication skills.

About the Role
We are seeking an experienced

Data Engineer

to join our team and support data-driven initiatives. This role involves building scalable pipelines, working with streaming data, and collaborating with data scientists and business stakeholders to deliver high-quality solutions.

Key Responsibilities
Design, build, and optimize

data pipelines and ETL workflows .
Manage and process large datasets using

Spark, PySpark, and SQL .
Build and maintain

real-time streaming applications

with Spark Streaming/Kafka.
Collaborate with data scientists and product teams to integrate AI/ML models into production.
Ensure

data quality, scalability, and performance

in all pipelines.
Deploy and manage Spark workloads on

cloud platforms (AWS, Azure, GCP, Databricks) .
Automate testing and deployment of Spark jobs via CI/CD pipelines.

Requirements
Bachelor's/Master's degree in Computer Science, Computer Engineering, or related field.
Minimum

4 years of professional experience

as a Data Engineer.
Strong expertise in

Python, Spark, PySpark .
Hands-on experience with

Spark SQL, DataFrames, UDFs, DAG execution .
Knowledge of

data ingestion tools

(Kafka, Flume, Kinesis) and

data formats

(Parquet, Avro, ORC).
Proficiency in

Airflow/Prefect

for scheduling and orchestration.
Familiarity with

performance tuning

in Spark (partitioning, caching, broadcast joins).
Experience deploying on

Databricks, AWS EMR, Azure HDInsight, or GCP Dataproc .
Exposure to

testing and CI/CD

for data pipelines (pytest, Jenkins, GitHub Actions).

Budget
Onshore:

As per market standards.

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