Techno-Functional Solution Leadership

Create a stakeholder map for a banking regulatory-reporting initiative, distinguishing decision makers, data owners, control functions, operators, and consumers.

Write a problem statement that separates business outcomes from requested technical features.

Map the current source-to-report workflow, including manual handoffs, delays, controls, and failure points.

Formulate discovery questions that expose ambiguous reporting rules, data assumptions, operational constraints, and customer expectations.

Classify candidate requirements as business, functional, non-functional, regulatory, or operational.

Define measurable product outcomes and leading indicators for reporting timeliness, accuracy, traceability, and operating effort.

Identify assumptions, dependencies, constraints, and unresolved decisions in a structured discovery log.

Define an initial product scope that distinguishes the minimum viable reporting capability from later enhancements.

Model the primary user journeys for report producers, data stewards, reviewers, approvers, auditors, and platform operators.

Build a shared domain glossary for submissions, reporting periods, data sets, validation rules, exceptions, attestations, and reports.

Translate a regulatory reporting rule into explicit data, calculation, validation, approval, and evidence requirements.

Write a functional use case with preconditions, main flow, exception paths, and postconditions.

Write testable acceptance criteria for a regulatory data-ingestion or reporting capability.

Prioritize a product backlog using business value, regulatory urgency, risk reduction, dependencies, and delivery effort.

Create a product risk register with owners, mitigations, triggers, and escalation thresholds.

Sequence product capabilities into incremental releases that deliver demonstrable business outcomes.

Model an immutable reporting domain in modern Java using records, enums, and sealed types where appropriate.

Use Java collections, streams, and Optional to implement readable validation and transformation logic.

Select an appropriate Java concurrency model for an I/O-bound ingestion or reporting workload.

Configure a Spring Boot application using dependency injection, typed configuration, profiles, and externalized secrets.

Implement a Spring Boot endpoint that validates a reporting submission against a versioned request contract.

Apply transaction boundaries and idempotency controls to prevent duplicate submission processing.

Add timeouts, retries, circuit breaking, and bulkheads to a Spring integration client based on failure semantics.

Write focused unit, slice, and integration tests for a Spring Boot reporting component.

Explain the lifecycle of regulatory data from source capture through submission, attestation, retention, and audit.

Trace a reported data element backward through transformations to its authoritative source.

Design a canonical data model that reconciles semantically different source-system representations.

Design a dimensional model that supports repeatable regulatory and management reporting queries.

Choose CSV, JSON, Avro, or Parquet for a data exchange based on schema, scale, interoperability, and query needs.

Define data-quality rules across completeness, validity, consistency, uniqueness, timeliness, and accuracy.

Design control totals and reconciliation checks that demonstrate source-to-report completeness.

Assess the impact of a regulatory rule change on source data, transformations, controls, approvals, and reports.

Explain how partitioning, parallelism, data locality, and fault tolerance enable distributed data processing.

Estimate task parallelism and identify where a distributed job will incur network shuffles.

Explain how HDFS blocks, replication, NameNodes, and DataNodes affect storage reliability and access.

Trace the lifecycle of a YARN application across the ResourceManager, ApplicationMaster, NodeManagers, and containers.

Configure the conceptual use of YARN queues, priorities, and resource limits for competing enterprise workloads.

Estimate cluster CPU, memory, storage, and executor needs for a bounded batch workload.

Diagnose a failed or slow Hadoop/YARN workload using application logs, counters, and resource symptoms.

Compare self-managed Hadoop/YARN with managed and cloud-native execution models using operational trade-offs.

Implement typed-schema DataFrame transformations for a regulatory data set using Spark.

Ingest malformed and evolving source data while isolating rejected records for investigation.

Implement joins, aggregations, and window calculations used in reconciliation and reporting.

Select partitioning, file sizing, pruning, caching, and persistence strategies for a Spark workload.

Diagnose data skew, excessive shuffling, spills, and poor executor utilization using a Spark execution plan and UI evidence.

Design a restartable ETL stage using idempotent writes, checkpoints, audit records, and deterministic transformations.

Implement a bounded Spark Structured Streaming flow with event-time windows, watermarks, and duplicate handling.

Write unit and data-quality tests for Spark transformations and verify them on representative input partitions.

Create current-state and target-state context diagrams for the regulatory reporting platform.

Convert business outcomes and non-functional requirements into measurable architecture quality-attribute scenarios.

Decompose the platform into cohesive data-plane, control-plane, workflow, reporting, and operational capabilities.

Select among files, REST APIs, events, streaming, and change-data capture for a specific integration need.

Define a versioned data contract and a compatibility policy for producers and consumers.

Select orchestration or choreography for a multi-stage reporting workflow based on coupling, visibility, and recovery needs.

Record an architecture decision with context, options, trade-offs, consequences, and review triggers.

Evaluate solution options using weighted criteria covering value, risk, security, operability, cost, and delivery constraints.

Map the platform's ingestion, processing, storage, catalog, query, reporting, and operations capabilities to suitable AWS service categories.

Design an Amazon S3 data-lake layout with landing, quarantine, raw, curated, and report zones.

Compare Amazon EMR, AWS Glue, and container-based Spark execution for a defined workload and operating model.

Choose between Amazon Athena, Amazon Redshift, and operational data stores for a specified access pattern.

Design metadata discovery and governed access using AWS Glue Data Catalog and AWS Lake Formation.

Design batch and event-driven workflow orchestration using appropriate AWS scheduling, messaging, and state-management services.

Design a resilient multi-account and multi-environment deployment topology for the platform.

Estimate major AWS cost drivers and propose scaling, retention, and workload-scheduling controls.

Create a threat model for data ingestion, transformation, reporting, and administrative workflows.

Design least-privilege human and workload access using AWS IAM roles, policies, and separation of duties.

Define network trust boundaries and private connectivity for data-platform components on AWS.

Choose encryption and key-management controls for data in transit, at rest, and in backups.

Design secure handling and rotation of application credentials, certificates, and third-party secrets.

Classify sensitive banking data and define masking, tokenization, retention, and deletion controls.

Design immutable audit evidence linking source data, transformations, approvals, code versions, and report outputs.

Map a regulatory or organisational control objective to preventive, detective, and corrective technical controls.

Distinguish predictive machine learning, generative AI, RAG, and agents when evaluating enterprise use cases.

Explain how tokens, embeddings, context windows, sampling, and hallucinations affect LLM application behaviour.

Use Python and an AWS model API to obtain schema-constrained output from a prompt.

Design a RAG pipeline that answers questions from versioned regulatory documents with source citations.

Evaluate retrieval relevance, answer faithfulness, refusal behaviour, latency, and cost using a small test set.

Design a tool-using agent that investigates a data-quality exception while requiring human approval for consequential actions.

Apply prompt-injection defenses, access controls, data protections, guardrails, and monitoring to an enterprise AI workflow.

Use AI assistance to accelerate requirements, design, coding, testing, and operations while independently verifying outputs.

Design a CI/CD pipeline with quality gates for Java services, Spark jobs, infrastructure, and data contracts.

Represent an AWS environment as version-controlled infrastructure using an infrastructure-as-code approach.

Define a test strategy spanning unit, contract, integration, data-quality, performance, security, and recovery testing.

Choose a safe release strategy for services, pipelines, schemas, and reporting-rule changes.

Define logs, metrics, traces, lineage events, and business-control telemetry for end-to-end observability.

Create service-level indicators and objectives for ingestion timeliness, pipeline completion, report availability, and data quality.

Run a structured incident triage that distinguishes code, data, dependency, capacity, and infrastructure failures.

Produce a root-cause analysis with evidence, contributing factors, corrective actions, owners, and recurrence checks.

Produce an integrated target architecture for the case study that connects business capabilities, applications, data flows, AWS infrastructure, controls, and operations.

Defend a major architecture choice using alternatives, evidence, trade-offs, risks, and explicit decision criteria.

Create an incremental delivery roadmap that coordinates business analysis, data, engineering, QA, security, platform, and operations work.

Define ownership boundaries, decision rights, review forums, and escalation paths for the product lifecycle.

Facilitate a design review by eliciting concerns, resolving conflicts, and recording decisions and follow-up actions.

Conduct a production-readiness review covering functionality, security, data controls, capacity, recovery, support, and rollback.

Lead a simulated production incident through communication, technical coordination, business impact management, and follow-up.

Present an executive solution recommendation that connects customer outcomes, regulatory obligations, architecture, investment, delivery risk, and measurable success.