Senior Manager – Data Engineering

About the Position

We are seeking a Senior Manager – Data Engineering to lead our enterprise data platforms, focusing on analytics, AI, and real-time use cases. You will own the strategy, architecture, and delivery of these platforms, blending deep technical credibility with senior people leadership, delivery ownership, and executive stakeholder engagement.

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

Requirements

• 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