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).