B.Sc. in Computer Science & Engineering or related field from any reputed university.

The ideal candidate will have 8 – 12+ years of professional experience with DWH development.

We are looking for dynamic individuals having the following qualities:

Proven experience in any Data Warehouse Development (e.g., Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse).
Mandatory experience with any cloud platforms (Azure, AWS, GCP) and their data services (e.g., S3, Glue, Dataflow, Databricks, BigQuery).
Mandatory experience in some big data technologies like Apache Spark, Apache Flink, Kafka Streams, or AWS Kinesis. etc.
Strong experience in OLTP, OLAP databases, and data modeling best practices (e.g., PostgreSQL, SQL Server, Oracle, MySQL).
Experience with batch or real-time ETL/ELT tools such as Azure Data Factory, Apache NiFi, Talend, Informatica, or dbt.
Expert in modern programming/scripting languages (e.g., Python, Scala, Java, Go, SQL).
Knowledge of CI/CD pipelines (e.g., Azure DevOps, Jenkins, GitHub Actions).
Familiarity with data science, AI/ML workflows, and platforms.
Exposure to DataOps and MLOps practices.
Experience with containerization and orchestration (e.g., Docker, Kubernetes, Airflow).
Experience with infrastructure-as-code tools (e.g., Terraform or CloudFormation).

What's in it for you?

Competitive salary.
A friendly work environment.
Weekend: Saturday & Sunday.

Application Deadline

September 30, 2026 (Early applications will be given preference)

Job Location

Gulshan-1, Dhaka

Number of Vacancies: 1

How would you apply?

If you believe you are the right candidate, then send your CV to career@relisource.com with this exact line as email's subject “DEA_10312025” by September 30, 2026.

IMPORTANT: Resume attached without above subject line will not be processed. Your CV must include your photograph.

Design and maintain data warehouses and data lakes for both structured and unstructured data.
Build and optimize ETL/ELT pipelines using batch and streaming data integration methods.
Lead the implementation of data models optimized for analytics, reporting, and machine learning.
Define and enforce data governance, data quality, and metadata management practices.
Implement appropriate tools and technologies for data ingestion, processing, storage, and access.
Monitor and tune system performance by ensuring high availability and disaster recovery for data platforms.
Collaborate with cross-functional teams including software engineering, analytics, and data science.

Data Architect

Summary

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