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Junior Data Integration Engineer
- Pay
- Pay not listed
- Work setting
Remote — work setting source
Work setup in the employer’s posting: TELECOMMUTE
From the employer’s posting- Employment
- Not stated
Before you apply
- New graduates
- Entry level
Job title: Junior Data Integration Engineer
What you’ll work on
Full postingBuild and maintain ETL/ELT pipelines that turn raw data into analytics-ready datasets
Develop and maintain data models with facts, dimensions, grains and keys
Write documented SQL transformations and analytics layers, using Python for automation
Apply version control, reusable patterns and basic automated tests to data work
Resolve data-related issues
From the employer’s posting
Build and maintain ETL/ELT pipelines to ingest and transform data into analytics-ready datasets
Develop and maintain data models (e.g., star schema concepts, facts/dimensions, grains, keys) for reporting and analytical use cases
Write clean, efficient, well-documented SQL for transformations and analytics layers; use Python where appropriate for automation and data handling
Apply analytics engineering best practices: version control, reusable patterns, documentation, and basic automated checks/tests
Resolve data-related issues effectively
Posting record
Report an errorActively hiringFirst seen by us 2 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 1, 20262 days ago
- Recorded sightings
- 4
- Last seen by us
- Nov 4, 2026
- Employer says posted
- Oct 14, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
EPAM Systems is hiring
656 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- AWS
- Azure
- BigQuery
- Databricks
- GCP
- Prefect
- Redshift
- Snowflake
- Airflow
Where the posting names them
We are seeking a Junior Data Integration Engineer to support the development and delivery of analytics-ready data products. You will work on ETL/ELT pipelines, transform and model data in a cloud data warehouse, contribute to trusted datasets and reporting layers used by the business and data issue resolution while building practical experience with modern data engineering tools, cloud solutions, and delivery best practices. Responsibilities Build and maintain ETL/ELT pipelines to ingest and transform data into analytics-ready datasets Develop and maintain data models (e.g., star schema concepts, facts/dimensions, grains, keys) for reporting and analytical use cases Gain practical proficiency with modern data engineering tools and technologies Apply analytics engineering best practices: version control, reusable patterns, documentation, and basic automated checks/tests Write clean, efficient, well-documented SQL for transformations and analytics layers; use Python where appropriate for automation and data handling Contribute to high-quality, timely software delivery projects Resolve data-related issues effectively Requirements Bachelor’s Degree in Information Technology, Computer Science or related field Experience in software implementation, delivery or related roles Proficient in Python and SQL with strong understanding of basic data structures Fundamental understanding of at least one software development lifecycle (SDLC) methodology, such as Waterfall or Agile Fundamental understanding of data modelling concepts (facts vs dimensions, grain, primary/foreign keys, normalization vs denormalization) Basic understanding of analytics/data lifecycle: source → transform → model → validate → publish/consume Proactive learning mindset with a strong desire to adopt new technologies and methodologies Demonstrated problem-solving skills and meticulous attention to detail Nice to have Experience in data warehousing implementation or data-related projects Familiarity with a cloud data warehouse (Synapse, BigQuery / Snowflake / Redshift / Databricks SQL) and basic performance concepts (partitioning, clustering, query cost) Familiarity with orchestration/tools (ADF, Airflow, Prefect) and data quality/testing concepts Experience with cloud platforms (AWS, Azure, GCP)
Your next step
Complete your application on careers.epam.com. The employer’s form will show what is required.
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Job description
We are seeking a Junior Data Integration Engineer to support the development and delivery of analytics-ready data products. You will work on ETL/ELT pipelines, transform and model data in a cloud data warehouse, contribute to trusted datasets and reporting layers used by the business and data issue resolution while building practical experience with modern data engineering tools, cloud solutions, and delivery best practices. Responsibilities Build and maintain ETL/ELT pipelines to ingest and transform data into analytics-ready datasets Develop and maintain data models (e.g., star schema concepts, facts/dimensions, grains, keys) for reporting and analytical use cases Gain practical proficiency with modern data engineering tools and technologies Apply analytics engineering best practices: version control, reusable patterns, documentation, and basic automated checks/tests Write clean, efficient, well-documented SQL for transformations and analytics layers; use Python where appropriate for automation and data handling Contribute to high-quality, timely software delivery projects Resolve data-related issues effectively Requirements Bachelor’s Degree in Information Technology, Computer Science or related field Experience in software implementation, delivery or related roles Proficient in Python and SQL with strong understanding of basic data structures Fundamental understanding of at least one software development lifecycle (SDLC) methodology, such as Waterfall or Agile Fundamental understanding of data modelling concepts (facts vs dimensions, grain, primary/foreign keys, normalization vs denormalization) Basic understanding of analytics/data lifecycle: source → transform → model → validate → publish/consume Proactive learning mindset with a strong desire to adopt new technologies and methodologies Demonstrated problem-solving skills and meticulous attention to detail Nice to have Experience in data warehousing implementation or data-related projects Familiarity with a cloud data warehouse (Synapse, BigQuery / Snowflake / Redshift / Databricks SQL) and basic performance concepts (partitioning, clustering, query cost) Familiarity with orchestration/tools (ADF, Airflow, Prefect) and data quality/testing concepts Experience with cloud platforms (AWS, Azure, GCP)
Source details
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- Location & working pattern
Malaysia
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