AVP-Data Integration

Role:

Responsible for leading the definition, development, continuous improvement, and delivery of enterprise data integration capabilities across Data Analytics platforms. The role acts as a senior individual contributor and technical platform owner for data integration, ensuring that data solutions are reliable, scalable, secure, cost-efficient, and aligned with business outcomes.

Responsible for designing, developing, and maintaining data integration architecture, data pipelines, data warehouse interfaces, data marts, and data exchange mechanisms between operational systems, analytical platforms, cloud platforms, and reporting/BI solutions.

Qualification:

Minimum Bachelor’s degree (S1) in Telecommunication, Computer Science, Computer Engineering, Information Technology, Data Engineering, or other relevant technical disciplines. Master’s degree or relevant professional certification in cloud/data engineering is an advantage.

Experience:

• Minimum 5–8 years of total experience in data engineering, data integration, Big Data, Data Warehouse, cloud data platform (GCP), or digital technology delivery.
• Minimum 3–5 years of hands-on experience in data integration architecture, ETL/ELT development, cloud data platform implementation (GCP), DWH/data mart development, or large-scale data pipeline delivery.
• Strong experience in managing end-to-end project delivery for telecom operator, enterprise data platform, or complex digital technology environment.
• Experience working with Agile delivery and/or structured project management methodology, including planning, estimation, risk management, delivery tracking, and production readiness.
• Experience with data governance, data quality, metadata, lineage, security/privacy, observability, and cost optimization / FinOps practices is preferred.

Working knowledge of AI/ML, advanced analytics, and real-time analytics use cases is an advantage.

Skills:

The role is expected to have sufficient hands-on technical depth to review, guide, and challenge engineering implementation, while primarily focusing on architecture ownership, technical governance, delivery assurance, and continuous improvement of enterprise data integration capabilities.

General Skillset:

1. Strong understanding of telecommunications business processes, enterprise IT landscape, data-driven business operations, and the role of data platforms in supporting commercial, finance, network, customer, digital, and regulatory use cases.
2. Strong analytical thinking and structured problem-solving capability to translate business problems into data integration requirements, architecture decisions, delivery plans, risks, dependencies, and measurable outcomes.
3. Strong stakeholder management, collaboration, negotiation, and communication skills to work effectively with business users, BI teams, data governance, enterprise architects, IT project owners, security, operations, vendors, consultants, and system integrators.

Must-have Skillset and Technology Expertise:

1. Data Integration Architecture: Strong capability in integration pattern selection, batch/incremental/near-real-time pipeline design, interface specification, data exchange mechanism, orchestration design, dependency management, resiliency pattern, and production readiness review.
2. ETL/ELT and Data Engineering: Hands-on experience designing, developing, reviewing, and optimizing ETL/ELT pipelines using GCP Platform, SQL, Python, PySpark/Spark, shell scripting, Talend, IBM DataStage, Apache Nifi, DBT, and/or equivalent enterprise data integration tools.
3. Cloud Data Platform and BigQuery: Strong knowledge of GCP data services, especially BigQuery, Cloud Storage, Cloud Composer / Apache Airflow, Dataflow, Pub/Sub, Dataproc, IAM, service account management, monitoring, logging, and cloud networking concepts relevant to data platforms.
4. Data Warehouse and Data Mart: Strong understanding of enterprise DWH, ODS, staging layer, data mart, dimensional modeling, star/snowflake schema, historical data handling, slowly changing dimensions, aggregation, semantic layer, reconciliation, and BI/reporting consumption patterns.
5. SQL, Programming, and Troubleshooting: Strong capability in SQL, Python, PySpark/Spark, API integration concepts, code review, transformation logic assessment, query/job optimization, failure analysis, root cause investigation, and development standard definition.