Data Engineer

JOB OBJECTIVE / SUMMARY

To design, build, and maintain robust, scalable, and high-performance ETL / ELT data pipelines for

reporting, business intelligence, and machine learning initiatives2. The role is critical for ensuring the

quality, lineage, and governance of all critical data assets.

DUTIES & RESPONSIBILITIES

i. Build and optimize data pipelines using tools like Airflow / Prefect to ingest data from core

banking, payment, and third-party sources.

ii. Design and implement dimensional and denormalized data models within the Data

Warehouse (e.g., Postgres / Oracle / BigQuery).

iii. Utilize streaming technologies like Kafka and transformation tools like DBT to process data in

real-time or near real-time.

iv. Implement data quality checks and maintain data lineage documentation for governance.

v. Leverage Python and SQL extensively for scripting, data manipulation, and pipeline

development.

KEY PERFORMANCE INDICATORS

KPI Area

1. Data Pipeline Reliability

2. Data Freshness & Delivery

3. Efficiency

Measure

1.Pipeline SLA Adherence (Uptime) & Percentage of automated tests (e.g., dbt tests).

2. Mean Latency for critical reports / data sets

3. Data Warehouse query run time (p95).

Target

1. >

99.9% Uptime, 90% of Critical Data Models Tested.

2. <1 hour for batch, <5 seconds for streaming data

3. Reduced by 20% QoQ.

EDUCATION AND EXPERIENCE

• Education : Bachelor’s degree or Higher National Diploma from Any Approved University or Polytechnic.
• Relevant certifications for DBA are an added advantage
• Experience : 4-6+ years hands-on experience in full stack development.
• Specific Experience : Prior experience in banking, fintech, or other financial services

environments is highly desirable.

KNOWLEDGE

Quality

SKILLS / COMPETENCIES

Postgres / Oracle / BigQuery, DBT, APIs, Git, CI / CD

Salary : Open to discussion