Company Technology and Strategic Direction Questions

High-level approach: produce a prioritized, actionable technical due-diligence checklist grouped by theme, with measurable benchmarks where possible, plus top red flags that should trigger walking away.

Top priorities (must-validate first)

1. IP & Licensing (critical)

• Verify ownership of code, algorithms, models, and datasets; check contributor agreements, employee contracts, third-party/OSS licenses (Apache, GPL, commercial).
• Confirm data licensing for any purchased/third-party datasets (usage, resale, geographic/time limits).
• Evidence: patent filings, assignment agreements, license files, SBOM for dependencies.

2. Security & Compliance (critical)

• Code repo access controls, MFA, secret management, VPC/network topology, encryption at-rest/in-transit (TLS, KMS).
• Compliance posture: SOC2/ISO27001/ GDPR/CCPA assessments, breach history.
• Run static analysis (SAST) and dependency vulnerability scan; sample dynamic tests (DAST).

3. Data Lineage & Quality (critical)

• End-to-end lineage (ingest → transform → output) tracked (e.g., OpenLineage, DataHub); schema evolution records.
• Data contracts, validation rules, SLAs for freshness/accuracy, observed data quality metrics (null rates, schema drift).
• Request sample provenance for key datasets.

Scalability & Performance (high)

• Benchmarks: representative workloads (peak and 95th/99th percentile), throughput (records/s), latency (per-batch, per-event), cost per TB processed.
• Test: run synthetic replay of historical peak load; measure CPU, memory, shuffle I/O (for Spark), autoscaling behavior.
• Check horizontal scaling limits, bottlenecks (single-node masters, metadata stores).

Operational Maturity (high)

• Runbooks, incident postmortems, on-call rota, SLOs/SLIs, backup/restore procedures, RTO/RPO targets and test evidence.
• Monitoring/alerting: metrics (Prometheus/GCP Stackdriver), logs (ELK), traces; paging and escalation flows.
• Disaster recovery: tested restores, backup retention, encryption keys handling.

Portability / Lock-in (medium)

• Architecture: cloud-native vs proprietary; use of managed services (Glue, BigQuery) vs open frameworks.
• Containerization, IaC (Terraform/CloudFormation), environment reproducibility, CI/CD pipelines; data exportability (formats: Parquet/Avro/ORC).
• Data egress cost estimates and effort to migrate.

Test Coverage & Code Quality (medium)

• Unit/integration/e2e test coverage metrics; presence of pipeline integration tests with realistic data; CI run pass rates.
• Code review policies, automated linters, benchmark regressions tests.

Integration Effort (medium)

• APIs, SDKs, ingestion adapters, data schemas mapping to our stack; identify adapters to ingest/export (Kafka, S3, GCS).
• Estimate effort (low/medium/high) by mapping components to our infra; list blockers (proprietary binary formats, encrypted datasets).

Deliverables to request

• Architecture diagrams, infra-as-code, access to a staging environment, sample datasets, historical runbooks/incidents, security reports, license inventory, benchmark scripts.

Top red flags (walk-away triggers)

• No clear ownership or conflicting claims on code or datasets, or irreversible 3rd-party license restrictions.
• Evidence of undisclosed breaches, repeated security incidents, or inability to produce security/compliance artifacts.
• No reproducible lineage/provenance for core datasets or evidence of data fabrication/unsourced data.
• Single-person knowledge silos with no documented runbooks or backups; inability to restore critical data.
• System cannot scale to our 95th/99th percentile workloads or required latency/cost is prohibitive.
• Extensive use of proprietary, non-portable components without migration path and vendor lock-in that prevents export of data/models.
• Absent or failing tests and CI, or tests that cannot be run by us (closed pipelines).

Summary: prioritize IP/security/lineage first, then scalability and ops maturity, and quantify integration risk with concrete benchmark runs and access to staging. Red flags above should trigger escalation to legal/exec teams and likely termination of the deal unless remediations are contractually guaranteed.

Objective (6 months): Improve platform reliability and reduce monthly data infra costs by 25% while maintaining data freshness and availability for consumers.

Measurable goals

• SLOs: 99.9% pipeline success rate (per critical pipeline) and 99.95% data availability for curated datasets.
• Error budget: <= 43.2 minutes/month per critical pipeline.
• Cost: Reduce monthly data infra spend by 25% (storage + compute for ETL/queries).
• Data retention: Reduce hot storage footprint by 30% via tiering and compaction.
• Observability: 100% of critical pipelines instrumented with SLIs and alerts.

Key initiatives (with month-by-month milestones)
Month 0–1: Discovery & alignment

• Audit: catalog pipelines, cost drivers, SLIs (2 FTEs, 3 weeks)
• Stakeholder kickoff: product, infra, SRE, analytics, finance
  Deliverable: prioritized heatmap (reliability x cost)

Month 2–3: Observability & SLO rollout

• Implement uniform telemetry (metrics/traces/logs) using existing tools (Prometheus/Datadog/OpenTelemetry) + pipeline-level SLIs (latency, success rate, lag)
• Create dashboards and alert runbooks (1 SWE + 1 Data Eng)
  Deliverable: Alerts for top 10 critical pipelines; SLOs published

Month 3–4: Cost optimization & storage policy

• Implement retention/ tiering: move cold data to cheaper object storage, compact small files (Spark compaction), enable partition pruning (2 Data Engs, 4 weeks)
• Rightsize clusters and adopt spot/preemptible instances for non-critical ETL
  Deliverable: 15–20% immediate cost reduction pilot

Month 4–5: Infra automation & reliability hardening

• CI/CD for pipelines, automated rollbacks, schema-change testing, canary runs (1 SRE + 2 Data Engs)
• Recoverability: add idempotent reprocessing, checkpointing improvements
  Deliverable: Reduced mean time to recovery (MTTR) target: <30 min for critical failures

Month 5–6: Measure, iterate, and scale

• Expand optimizations to next priority tier, automate cost reporting, runbook training for on-call
• Postmortem cadence and continuous improvement
  Deliverable: Reach 99.9% pipeline success and cumulative 25% cost reduction

Resource estimates

• Core team: 3 dedicated Data Engineers, 1 SRE part-time, 0.5 Product/Analytics PM, ~0.2 Finance for cost tracking
• Tooling: modest investment in observability and CI/CD ($20–50k one-time + licensing) or configuration of existing stack

Cross-functional buy-in plan

• Start with a one-week discovery and a prioritized heatmap to show impact and ROI.
• Hold biweekly stakeholder syncs with clear KPIs; monthly business-facing report (SLOs, cost delta).
• Run 2-week pilots with data consumers to validate no-regression on SLAs.
• Use a “safety net” policy: Canary optimizations and quick rollback to gain trust.
• Success metric alignment with finance (cost savings) and analytics (data freshness/availability) to secure ongoing funding.

Risks & mitigations

• Risk: consumer pushback on retention changes — mitigate via archived-access workflow and fast restore SLA.
• Risk: tooling overload — prioritize incremental instrumentation and reuse existing platforms.

This roadmap balances reliability, measurable cost savings, and stakeholder alignment through short pilots, transparent KPIs, and automated safeguards.

KPIs (with targets): Platform reliability: uptime (%) >99.95, mean time to recover (MTTR) <30m. Adoption: active analysts/week, percent of teams using standardized datasets (>80%). Pipeline health: jobs success rate >99%, average data freshness SLA compliance. Privacy incidents: incident count = 0, mean time to remediation <24h. Dashboard layout: Top row — executive summary tiles (uptime, adoption %, privacy incidents). Second row — trend charts: uptime and MTTR over 90 days; active analyst growth. Third row — pipeline health matrix: job success rate heatmap by domain, SLA compliance bar chart. Fourth row — privacy & compliance: open incidents list, time-to-remediation, top risky datasets. Actionable drill-downs: click uptime → affected services + recent logs; click job failure rate → failing job stack trace, owner, last successful run; click adoption → per-team usage and blocked tickets. Visualizations: sparklines for trends, heatmaps for hotspots, bar charts for percent metrics, and a table for actionable items with owner and SLA. Include automated alerts (Slack/email) when thresholds breach.

I use a 5-step repeatable research process:

1. Clarify goals: what infra topics matter for a Data Engineer (data ingestion, processing, storage, orchestration, observability, cost/scale).

2. Prioritized sources (quick → deep):

   • Engineering blog posts (design decisions, migration case studies)
   • Conference talks / YouTube (architecture diagrams, lessons learned)
   • Open-source repos / GitHub orgs (ETL code, Helm charts, CI configs)
   • Job descriptions (tech stack, scale, gaps in team skills)
   • Cloud provider case studies / partner pages (deployment patterns, telemetry claims)

3. Verification techniques:

   • Cross-check architecture claims across 2+ sources (blog + talk + repo)
   • Inspect repo activity (commits, issues, recent PRs) to validate maintenance
   • Look for artifacts: Terraform/Helm manifests, DAGs, schema migration scripts
   • Search public infra telemetry (status pages, SLO/incident reports, tech posts about outages)
   • Validate scale claims via job postings (number of roles), case studies, and benchmarks in talks

4. Synthesis into 3–5 interview talking points (concise, actionable):

   • "I noticed you use X (e.g., Spark on EMR/GKE) for batch processing — I’ve optimized Spark jobs via adaptive query execution and partitioning to reduce costs and would approach X by… (brief tactic)."
   • "Your engineering blog describes a move to Y data lake/storage — I’d ask about schema evolution/partition strategy and propose a governance plan (CI for schema, compaction jobs)."
   • "I saw open-source DAGs/ETL in the repo — I’d suggest adding observability: lineage, SLA alerting, and row-level metrics to debug data quality faster."
   • "Job descriptions emphasize real-time pipelines — I’d discuss trade-offs between Kafka vs managed streaming, exactly‑once semantics, and backpressure handling."
   • "If cost/scale is a focus (from case study), I’d propose measuring per-job cost, rightsizing clusters, and introducing autoscaling + spot/preemptible instances."

5. Preparation checklist: bookmark sources, extract 1–2 concrete questions per talking point, and prepare a short example from your experience that maps to each.

Primary governance principles: privacy-by-design, data minimization, clear ownership, provenance and lineage, purpose limitation, and accountability. Short governance checklist before approving a new data use-case:

1. Purpose clarity: Is the business question defined and justified?
2. Data minimization: Are we only collecting necessary fields?
3. Privacy risk: Can individuals be re-identified? Apply DP or aggregation thresholds.
4. Legal/compliance review: Meets regulatory and internal policy (GDPR, CCPA).
5. Ownership & retention: Has an owner and retention window been set?
6. Security controls: Access levels and encryption in place?
7. Measurement plan: Defined metrics, success criteria, and rollback conditions. Approval requires sign-off from data owner, privacy officer, and product lead.