Head - Data Practice & Delivery

KEY RESPONSIBILITIES: Data Platform Delivery & Cloud-Native Engineering: - Own end-to-end delivery of enterprise data transformation programs on AWS, Azure, and GCP. - Architect and implement modern data platforms: Lakehouse (Databricks / Delta Lake), Cloud Warehouses (Snowflake, BigQuery, Synapse), and hybrid data meshes. - Design and govern scalable data pipelines using Apache Spark, dbt, Airflow, Azure Data Factory, AWS Glue, and Dataflow. - Drive DataOps and data engineering best practices: CI/CD for data, automated testing, lineage, and observability. - Ensure real-time and streaming data architectures using Kafka, Kinesis, or Azure Event Hubs where required. Data Architecture & Technical Leadership: - Define and enforce data architecture standards across Lakehouse, Data Vault 2.0, dimensional modelling, and data mesh patterns. - Lead cloud-native data platform design across AWS (Redshift, Lake Formation, Glue), Azure (Synapse Analytics, Purview, Fabric), and GCP (BigQuery, Dataplex, Vertex AI Feature Store). - Oversee data governance, data quality frameworks, metadata management, and master data management (MDM) programs. - Evaluate and recommend data tools, frameworks, and accelerators to improve delivery speed and platform resilience. - Guide teams on advanced analytics patterns, semantic layer design, and self-serve BI enablement. Data Adoption Across Accounts: - Identify and drive high-impact data platform modernisation opportunities within existing and new accounts. - Enable delivery teams to adopt data-first engineering practices and cloud-native data tooling. - Embed data quality, lineage, and observability standards into every engagement from day one. Presales & Customer Engagement: - Lead data-focused solutioning, effort estimation, and proposal responses for RFPs and new opportunities. - Facilitate customer data discovery workshops, architecture design sessions, and executive briefings. - Build trusted advisor relationships with Chief Data Officers, VPs of Engineering, and Analytics leadership. - Articulate the value of modern data platforms in business terms - cost reduction, time-to-insight, and data product ROI. Capability Building & Team Growth: - Build and scale a high-performing team of data architects, data engineers, analytics engineers, and data governance leads. - Upskill teams on modern data stack tools: Databricks, Snowflake, dbt, Great Expectations, Apache Iceberg, and hyperscaler data services. - Establish an internal Data Centre of Excellence (CoE) with reusable assets, reference architectures, and playbooks. - Drive hiring, performance management, career development, and succession planning within the practice. WHAT WE'RE LOOKING FOR: Must-Have: - 15+ years in data engineering, data architecture, analytics, or cloud data platform delivery. - Proven hands-on experience designing and delivering Lakehouse, Data Warehouse, and Data Mesh architectures at scale. - Mandatory depth in modern data platforms and tooling: 1. Cloud Data Warehouses: Snowflake, BigQuery, Azure Synapse, or Amazon Redshift. 2. Data Orchestration: Apache Airflow, Azure Data Factory, AWS Glue, or Prefect / Dagster. 3. Data Transformation: dbt (data build tool) - models, tests, documentation, and lineage. 4. Big Data Processing: Apache Spark (PySpark) on Databricks, EMR, or Dataproc. 5. Streaming: Kafka, AWS Kinesis, or Azure Event Hubs. - Strong command of cloud data ecosystems across AWS, Azure, and/or GCP. - Deep expertise in data governance, data quality (Great Expectations / Monte Carlo), and metadata management. - Track record leading multi-team data delivery programs and winning client confidence. - Strong presales and executive communication skills. You Will Stand Out If You Have: - Hands-on experience with Apache Iceberg, Delta Lake, or Apache Hudi for open table format architectures. - Familiarity with DataOps tooling: data contracts, automated data quality gates, and observability platforms. - Exposure to AI/ML feature engineering, Vertex AI Feature Store, or MLflow in a data delivery context. - Experience standing up a Data CoE or data practice from the ground up. - Background in IT services, consulting, or GCC environments with commercial accountability. - Certifications in Databricks, Snowflake, or hyperscaler data platforms (AWS / Azure / GCP).