IT Platform Lead

Overview

The IT Platform Lead will be responsible for designing, building, modernizing, and operating enterprise-scale data engineering platforms that support analytics, AI/ML, regulatory reporting, and data-driven business use cases. Ensures data platforms are scalable, secure, resilient, compliant, and aligned with European banking regulatory, governance, and operational standards.

Provides technical leadership across data engineering, cloud platforms, governance, operations, modernization, and platform reliability.

Job Description

Key Responsibilities

Own and drive enterprise data platform strategy, roadmap, standards, and implementation approach.

Design scalable, secure, resilient, and high-performing data architectures across data lake, data warehouse, and lakehouse platforms.

Lead end-to-end data engineering platform development, operations, modernization, and continuous improvement.

Build and manage robust ETL/ELT pipelines for analytics, reporting, AI/ML, finance, risk, and regulatory use cases.

Strong hands-on experience in data engineering, data integration, transformation, ingestion, and data distribution patterns.

Advanced SQL expertise for complex querying, data modelling, performance tuning, reconciliation, and analytical workloads.

Strong programming experience in Python with exposure to PySpark, Spark, Scala, or Java.

Deep understanding of batch processing, incremental loads, CDC, pipeline orchestration, and data pipeline design.

Experience building scalable pipelines for structured, semi-structured, and unstructured data.

Strong knowledge of data modelling, dimensional modelling, data marts, enterprise data warehouse concepts, facts, and dimensions.

Hands-on experience with big data and distributed processing technologies such as Spark, PySpark, Hadoop, or equivalent platforms.

Experience with streaming and event-driven platforms such as Kafka or similar messaging technologies.

Experience with workflow orchestration and scheduling tools such as Airflow, Control-M, TWS, or equivalent tools.

Strong experience with cloud data platforms across Azure, AWS, or GCP.

Hands-on experience with modern data platforms such as Snowflake, Databricks, Azure Synapse, BigQuery, Redshift, or equivalent platforms.

Experience with CI/CD, DevOps, Git, automated deployments, infrastructure-as-code, release management, and platform automation.

Strong understanding of data governance, data quality, metadata management, lineage, reconciliation, monitoring, and observability.

Knowledge of GDPR, data privacy, access control, encryption, masking, retention, audit controls, and security-by-design principles.

Strong understanding of banking data domains including Payments, Cards, Lending, Credit, Finance, Risk, Regulatory Reporting, Treasury, and Investments.

Proven leadership in managing data engineering teams, stakeholder engagement, architecture governance, vendor coordination, platform reliability, and cost optimization.

Leadership & Platform Ownership

Ability to translate business, regulatory, and operational requirements into scalable technical solutions.

Ability to balance delivery timelines, platform stability, regulatory priorities, technical debt, and modernization goals.

Experience working in complex, multi-country, enterprise banking or financial services environments.

Strong communication, problem-solving, decision-making, and technical governance capabilities.

Preferred Skills

Exposure to Dataiku for analytics workflows and enterprise data platform integration.

Exposure to MicroStrategy, Power BI, Tableau, or similar BI/reporting platforms.

Experience with dbt for transformation, modelling, and governed analytics engineering.

Knowledge of container platforms, Kubernetes, OpenShift, or modern platform engineering practices.

Experience in data platform migration, legacy modernization, and cloud transformation programs

Experience Required

12-14+ years of experience with strong focus on data engineering, data platforms, analytics, and enterprise data architecture.

3+ years of technical leadership or platform ownership experience in large enterprise environments.

Education & Certifications

Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, Data Engineering, or related field.

Preferred certifications: Azure Data Engineer, AWS Data Analytics, Google Professional Data Engineer, Snowflake, Databricks, Spark, Data Governance, Cloud Architecture, or DevOps certifications.

Skills & Requirements

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