Key Responsibilities

Design, develop, and maintain scalable data pipelines and ETL/ELT processes for ingestion, transformation, and integration of data from multiple sources.
Write clean, efficient, and well-documented code in Java, Python, Scala, PL/SQL, and SQL for data processing and automation tasks.
Create workflow diagrams and data flow mappings to demonstrate pipeline functionality before implementation.
Conduct data quality checks, testing, and debugging to ensure pipeline reliability, accuracy, and performance.
Perform regular data audits and performance tuning to identify inefficiencies and propose workflow improvements.
Collaborate with business and analytics teams to gather requirements and translate them into scalable data engineering solutions.
Maintain clear technical documentation and create user guides/tutorials for developed data processes.

Job Requirements

Diploma/bachelor’s degree in computer science, Information Technology, or related field.
Strong development experience with SQL, PL/SQL, Python, Java, Scala.
Good knowledge of data modeling (dimensional & relational), including stored procedures, constraints, normalization, indexes, and data security.
Strong understanding of data flows, integration, and governance best practices.

Preferred / Nice-to-Have Skills

Experience with ETL tools such as Talend, Informatica, Apache NiFi, dbt.
Knowledge of job orchestration & scheduling tools (e.g., Control-M, Airflow, Prefect, Luigi).
Exposure to big data platforms (Hadoop, Spark, Hive) for large-scale data ingestion.
Hands-on experience integrating data from AS400, Oracle Finance, MS SQL, SAP, or API-based systems.
Familiarity with cloud-native data platforms (AWS Glue, AWS Redshift, Azure Data Factory, GCP Dataflow/BigQuery).
Understanding of open-source data frameworks (Kafka, Flink, Debezium, Trino/Presto).
Basic knowledge of containerization and DevOps practices (Docker, Kubernetes, CI/CD pipelines).

Other Requirements

Data Engineer