Senior Manager – Data Engineering

The Senior Manager – Data Engineering will own the strategy, architecture and delivery of enterprise data platforms that power analytics, AI and real-time use cases. You will lead multiple data engineering teams, set technical direction across streaming, batch and lakehouse layers, and be accountable for platform reliability, data quality and governance. The role blends deep technical credibility with senior people leadership, delivery ownership and executive stakeholder engagement.

Key Responsibilities

• Define and own the data engineering vision, strategy and multi-phase roadmap across analytics, AI and real-time data platforms
• Lead the design and operation of real-time streaming pipelines using Apache Kafka / Amazon MSK and Debezium-based change data capture (CDC)
• Architect and govern the data lake and lakehouse on S3, using Apache Iceberg, Parquet and Avro, with AWS Glue and Athena for cataloguing and query
• Oversee analytical and serving layers across Redshift, BigQuery and NoSQL / RocksDB-based stores, choosing the right engine for each workload
• Drive data modeling standards for analytical, operational and real-time use cases
• Orchestrate batch and streaming workflows with Apache Airflow, with clear SLAs, retry and backfill practices
• Guide development of custom Java and Python frameworks for ingestion, transformation, testing and data quality
• Establish enterprise standards for data quality, governance, observability and lineage, including compliance with financial-sector and regional data protection requirements
• Lead deployment of data services on ECS / EKS using Docker and infrastructure as code (Terraform / CDK), with automated CI/CD
• Own platform cost, performance and capacity management, and report on platform health to senior leadership
• Build, mentor and retain high-performing data engineering teams, including hiring, performance management, career development and design/code review standards
• Partner with data science, AI, product, risk and business leaders to turn requirements into platform capabilities
• Evaluate and introduce new technologies and open-source platforms where they add clear value, with defined adoption and exit criteria
• Manage vendors, budgets and delivery risk across engagements

Required Skills and Qualifications

• 12+ years of experience in data engineering, including at least 5 years leading or managing engineering teams, ideally including managers or multiple squads
• Strong hands-on background in Apache Kafka / Amazon MSK and Debezium CDC
• Deep experience with Amazon Redshift, Google BigQuery, AWS Glue, Athena and S3
• Experience with NoSQL / RocksDB data stores
• Strong programming skills in Java and Python
• Production experience with Apache Airflow
• Working knowledge of Apache Iceberg and data lake architecture
• Solid understanding of streaming and batch processing, data modeling and columnar/serialization formats (Parquet, Avro)
• Proven experience implementing data quality, governance, observability and lineage frameworks
• Experience with ECS / EKS, Docker and Terraform / CDK
• Bachelor's or Master's degree in Computer Science, Engineering or a related field

Preferred Qualifications

• FinTech or Financial Services experience, including exposure to regulated, high-volume, low-latency data environments (payments, banking, trading or risk data)
• Experience supporting AI/ML workloads and feature or model data pipelines

Nice to Have

• dbt for transformation and analytics engineering
• Apache Pinot, Trino / Presto, ClickHouse or Apache Druid
• GitHub Actions and CI/CD pipeline design
• Terraform / CloudFormation at scale
• AWS or GCP certifications (Data Analytics, Solutions Architect, Professional Data Engineer)
• Experience delivering projects in the GCC or MENA region

Soft Skills

• Executive-level communication and stakeholder management
• Strong leadership, coaching and team-building skills, including in remote or distributed teams
• Strategic thinking combined with practical, delivery-focused judgment
• Ownership mindset, accountability and comfort making architectural trade-offs