KEY RESPONSIBILITIES

 

      I.          Define and maintain the enterprise data architecture strategy (cloud, hybrid, on-premises).

    II.          Design data platforms (Data Lake, Data Warehouse, Lakehouse) and integration patterns.

  III.          Establish data engineering standards and best practices (ETL/ELT, streaming, transformation).

  IV.          Define enterprise data quality framework and governance model.

    V.          Design data cleansing, standardisation, and master data management strategies.

  VI.          Define and oversee data migration architecture and strategy for legacy system modernisation.

VII.          Establish data modeling standards (conceptual, logical, physical models).

VIII.          Define data validation, monitoring, and quality scorecards.

  IX.          Ensure data lineage, traceability, and auditability across systems.

    X.          Select and govern data technologies and platforms (AWS, GCP, Oracle, etc.).

  XI.          Optimize performance, scalability, and cost efficiency of data platforms.

XII.          Define data governance, metadata, and catalog strategies.

XIII.          Provide technical leadership and guidance to Data Engineers and other teams.

XIV.          Collaborate with business and IT stakeholders to align data architecture with business goals.

XV.          Carry out such acts as shall be required for the proper fulfilling of duties listed above.

 

 

RESOURCE ALLOCATED (Standard as allocated to employees within Job Level)

 

 

QUALIFICATIONS & EXPERIENCE

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or equivalent.
• 7 to 10+ years in Data Engineering, BI, or Architecture roles
• Minimum 3–5 years in a Data Architect or senior design role
• Strong experience in data migration and legacy modernization projects

 

TECHNICAL SKILLS

·       Strong expertise in enterprise data architecture design (Data Lake, Data Warehouse, Lakehouse).

·       Advanced knowledge of data modeling methodologies

·       Deep understanding of ETL/ELT architecture and data integration patterns (batch, streaming, API-based).

·       Strong experience with cloud data platforms, including one or more of:

o   AWS (S3, Glue, Redshift, EMR, Kinesis, DMS)

o   GCP (BigQuery, Dataflow, Dataproc, Pub/Sub)

o   Oracle OCI (Autonomous Data Warehouse, GoldenGate, Data Integration)

·       Expertise in data migration strategies and tools, especially for legacy systems (e.g., Oracle databases).

·       Strong knowledge of data governance frameworks, including data quality, metadata, and lineage management.

·       Experience designing data quality frameworks, including validation rules, profiling, and monitoring.

·       Knowledge of data cleansing and standardisation strategies at enterprise level.

·       Familiarity with big data and distributed processing frameworks (Apache Spark, Kafka).

·       Experience with data catalog and governance tools (e.g., Glue Catalog, Dataplex, Oracle Data Catalog).

·       Strong understanding of performance optimization techniques (partitioning, indexing, query tuning).

·       Knowledge of security and access control mechanisms for data platforms.

·       Familiarity with modern data stack tools (Airflow, dbt, Delta Lake, Iceberg).

 

 

 

COMPETENCIES

• Strong problem-solving and analytical skills
• Excellent communication and stakeholder management skills, with the ability to translate business needs into technical solutions.
• Strong leadership and mentoring abilities to guide Data Engineers and technical teams
• Ability to work under pressure and meet deadlines
• Adaptability and willingness to learn new technologies
• Customer-focused mindset