Senior Data Engineer

About This Job

Meezan Bank Limited has advertised a Senior Data Engineer position within its Data Analytics - Finance Department, based at the Head Office in Karachi. Posted on the bank's careers portal on 25 September 2026, this is a more senior counterpart to the bank's other Data Engineer opening, aimed at candidates who can independently lead complex data-integration work rather than execute under close supervision.

Applicants need at least a bachelor's degree in Computer Science, Information Technology, or Software Engineering from an HEC-recognized institution, with Islamic finance knowledge viewed favourably. The bank expects seven or more years of experience in data engineering, ETL/ELT, data warehousing, and enterprise integration, with practical command of IBM DataStage, DB2W, and Big SQL across batch and real-time pipelines, complex transformations, CDC/incremental processing, API and file integration, streaming, distributed processing, data migration, automated validation, and source-to-target reconciliation. The position is open to men and women from any domicile, with no age restriction stated. Core duties involve leading complex ETL/ELT and real-time integration work across DB2W, Big SQL, DataStage, and Cognos, working with data modelers, architects, BI, and governance teams, setting up data-quality controls and reconciliation frameworks, and rolling out automated data-integrity monitoring for critical pipelines.

To apply, candidates should go to meezanbank.com/careers, follow the "Explore Current Opportunities" link to the bank's Oracle Cloud recruitment site, locate this Senior Data Engineer posting, and submit their application online with a CV. There is no application fee. The closing date is 21 October 2026, and experienced data engineers from a banking or financial-services background are encouraged to apply before the window closes.

Job Information

Commission / Board

Meezan Bank

Place of posting

Head Office, Karachi

City

Karachi

Postal Code

74000

Domicile

All Punjab Basis

Age (Male)

18–35 years

Age (Female)

18–35 years

Salary / BPS

Negotiable

Advt. date

25 September 2026

Last date

21 October 2026

Qualification Required

• Qualification: Minimum Bachelor's degree in Computer Science, Information Technology, or Software Engineering from an HEC-recognized institute/university. Qualification in Islamic Finance will be an added advantage.
• Experience: 7+ years' experience in Data Engineering, ETL/ELT, Data Warehousing, and enterprise integration, with hands-on expertise in IBM DataStage, DB2W, Big SQL, batch/real-time pipelines, complex transformations, CDC/incremental processing, API/file integration, streaming, distributed processing, migration, data quality, automated validation, source-to-target reconciliation, and complex integration scenarios.

Important Dates

1. Advertisement Published25 September 2026
2. Last Date to Apply21 October 2026

How to Apply

1. Visit the Meezan Bank careers portal at meezanbank.com/careers and select "Explore Current Opportunities", which opens the Bank's Oracle Cloud recruitment site.
2. Search for the "Senior Data Engineer" posting under the Data Analytics - Finance Department.
3. Create a candidate profile or sign in, then submit your application with your CV before the closing date of October 21, 2026.

Additional Details

Skills Required

Strong proficiency in SQL, IBM DataStage, DB2W, Big SQL, and ETL/ELT. Expertise in data warehousing, modelling, and batch/real-time data integration. Proficiency in data quality, profiling, validation, cleansing, reconciliation, and control checks. Knowledge of data lineage, governance, CDC, APIs, streaming, distributed processing, and lakehouse architectures.

Key Responsibilities

Leading complex ETL/ELT, batch, real-time, and enterprise data integration across DB2W, Big SQL, DataStage, Cognos, and other technologies. Collaborating with stakeholders, Data Modelers, Architects, BI, and Governance teams to deliver scalable, agile, governed, and high-performance data solutions. Establishing data quality controls, validation frameworks, source-to-target reconciliation, balancing checks, and exception handling. Implementing automated data integrity monitoring across critical data pipelines.

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