Technical Delivery Manager - Data Lakehouse and Data Engineering

Custom Software Engineer

Project Role : Custom Software Engineer
Project Role Description : Develop custom software solutions to design, code, and enhance components across systems or applications. Use modern frameworks and agile practices to deliver scalable, high-performing solutions tailored to specific business needs.
Must have skills : Data Engineering
Good to have skills : Oracle Procedural Language Extensions to SQL (PLSQL), Salesforce CRM Analytics, SAS DataFlux ETL Tools
Minimum 15 year(s) of experience is required
Educational Qualification : 15 years full time education

Role Summary
Technical Delivery Manager will lead the end to end implementation of the Bank s Enterprise Data Lakehouse program, managing delivery governance, technology integration, and cross functional execution. The role blends program leadership with strong hands on technical expertise across data platforms, integration technologies, analytics, and governance frameworks.
Key Responsibilities
Lead the overall delivery of the Data Lakehouse program, supported by project managers and SMEs.
Drive weekly status reviews, risk management, change control, and program level reporting.
Coordinate with Bank stakeholders, manage escalations, and participate in governance forums including steering committees.
Oversee scope execution across ingestion (batch, CDC, streaming), ETL/ELT, MDM, governance, analytics, reporting, and downstream integration.
Required Qualifications
Minimum 15+ years Program Management experience in Data Warehouse/Data Lake/Data Lakehouse programs in Scheduled Commercial Banks (1000 branches).
MBA or Engineering Graduate with PMI / Prince2 certification.
Technical Skills
1. Data Engineering & Ingestion
Deep understanding of ETL/ELT frameworks, transformation logic, error handling, validation, and job scheduling.
Hands on experience with CDC (Change Data Capture), batch processing, streaming ingestion, and workflow orchestration.
Familiarity with parallel processing, in database processing, MapReduce interfaces, and optimization for large volumes.
2. Data Lakehouse Architecture
Clear knowledge of Bronze–Silver–Gold architecture, distributed storage, metadata layers, ACID tables, versioning, snapshots, and partitioning.
Ability to interpret and validate architecture covering table formats, schema evolution, concurrent read/write, and federated queries.
3. Data Governance, MDM & Data Quality
Experience in Master Data Management, metadata management, data lineage, data quality rules, standardization, survivorship, and golden record creation.
Knowledge of governance frameworks covering access control, audit trails, approvals, and secure workflows.
4. Analytics, BI, and AI
Understanding of analytics workloads including ML, predictive modeling, in memory computing, and analytical data processing (cleansing, transformations).
Familiarity with open-source analytical ecosystems (Python, statistical libraries, dashboarding tools).
Knowledge of BI/reporting layers, dashboards, and KPI design.
5. Infrastructure, Platform & Cloud
Working knowledge of distributed architecture, storage tiers, replication, redundancy, hardware sizing, workload management, and performance tuning.
Understanding of multi vendor hardware, virtualization, backup/archival systems, and DR/DC deployments.
6. Security, Compliance & Observability
Understanding of operational security, SIEM integrations, vulnerability remediation, DR testing, and compliance with ISSP/Bank security policy.
Experience with workload monitoring, observability tools, incident/problem/change management (ITSM aligned).
7. Programming & Scripting
Good working familiarity with data engineering and analytical programming languages (Python, R, Scala, Spark, SQL) to guide technical teams.
Preferred Competencies
Strong architectural evaluation capability for banking-scale data platforms.
Ability to manage multi-vendor delivery teams and complex technology ecosystems.
Excellent stakeholder communication and executive reporting skills.