The Next Decade of Data Engineering: Agentic Systems and GenAI Pipelines
Product teams are hitting a ceiling, and it isn't model quality. It's data fragmentation. In a recent conversation, Sohag Maitra-Senior Data Analytics Engineer at Rabobank and long-time builder across product, software, and data-laid out a path that's practical, measurable, and within reach.
The shift is clear: from monolithic stacks to cloud-native platforms, and now toward intelligent, self-managing systems that reduce manual orchestration. If you own product outcomes, this matters more than ever. Faster data-to-decision is a direct line to faster shipping and better customer results.
From Monoliths to Modern Data: Why Product Should Care
Sohag's career mirrors the transformation of enterprise data. We've moved from rigid, on-prem systems to flexible cloud architectures. That flexibility is great-until you're drowning in choices, tools, and handoffs.
The lesson: modernization isn't a lift-and-shift. It's a rethink. Governance, data quality, and security must be built into distributed systems from day one, or your team ends up firefighting instead of shipping.
The Real Blocker: Data Fragmentation
Most ML teams spend the majority of their time wrangling data instead of building. That gap kills velocity. It also creates a hidden tax on product timelines, because features and experiments stall at the "where is this data and can we trust it?" stage.
- Single source of truth: pick a lakehouse standard and commit.
- Data contracts: treat schemas like APIs with owners, SLAs, and versioning.
- Lineage and quality: observable by default, not an afterthought.
Blueprint: Enterprise Feature Platforms That Actually Ship
Sohag showcased a path at ML Con: unify feature engineering on top of modern table formats and a shared registry. Think Delta/Iceberg tables plus a feature store, wired into CI/CD and observability.
The results are hard to ignore: about 70% faster time-to-deploy models and roughly 3x feature reuse across teams. For product, that translates to fewer net-new builds and more repeatable launches.
- Standardize data contracts at ingestion (events, CDC, batch). Enforce schema evolution.
- Adopt a feature registry for discovery, reuse, and governance.
- Use lakehouse tables with ACID and time travel for reproducibility.
- Automate data CI/CD (tests, deployments, rollbacks) like you do for app code.
- Instrument data quality monitors and SLOs tied to product KPIs.
If you need a starting point for table formats, explore Delta Lake and Apache Iceberg. Pick one and move.
What's Next: Intelligent, Self-Managing Data Systems
The direction is agentic: platforms that optimize themselves, enforce policies, and make context-aware decisions without manual reconfiguration. Large language models add a simple interface layer so teams can ask questions in plain language and get trustworthy answers.
- Policy-as-code: automatic governance and access controls driven by metadata.
- Observability with auto-remediation: detect drift, quarantine bad data, suggest fixes.
- Conversational data access: natural language interfaces backed by lineage and policy.
Architecture Bets for the Next 3-5 Years
- Lakehouse as standard: Delta, Iceberg, or Hudi for unified analytics and governance.
- Real-time streaming: low-latency features and decisions as a first-class capability.
- GenAI in the stack: AI-assisted pipeline design, data quality suggestions, and architectural recommendations with human-in-the-loop approval.
- Privacy-preserving AI: techniques like federated learning to work with sensitive data responsibly.
Org Strategy: How Product Accelerates This
Sohag argues for a tight loop between industry, universities, and public sector to push modern practices forward. Open-source continues to lead the way-Apache projects have moved the field faster than any single vendor.
For product orgs, the play is simple: invest in platform capabilities that remove friction for feature teams. Measure reuse, time-to-first-feature, and data incident rates as product metrics.
Your 90-Day Plan
- Weeks 1-2: Map data fragmentation. Choose your lakehouse format. Define top three use cases and required features.
- Weeks 3-6: Stand up a feature store proof-of-concept. Set reuse and lead-time targets. Add lineage and basic SLOs.
- Weeks 7-10: Add streaming for one use case (events or CDC). Backfill strategy, schema versioning, replay testing.
- Weeks 11-13: Pilot an LLM-powered data assistant with guardrails. Implement policy-as-code for access and governance.
Questions to Pressure-Test Your Roadmap
- Where does the source of truth live, and who owns the data contract?
- What's our lead time from raw data to a reusable feature in production?
- How do we prevent rebuilding the same features across teams?
- What's our data SLO stack: freshness, completeness, and accuracy targets?
- How do we keep sensitive data compliant while enabling experimentation?
- Which manual runbooks could agents automate safely in the next quarter?
Advice for Builders and Founders
Go deep on Python, SQL, and your primary cloud. Go broad on systems, product thinking, and data governance. Don't chase shiny tools; solve recurring problems and measure the impact.
Look for opportunities in embedded analytics, industry data platforms, and AI-powered data ops. Edge use cases are heating up-real-time decisions for IoT and autonomy will demand the same data discipline, just closer to the source.
Bottom Line
Data fragmentation is a tax on product velocity. Unified lakehouse foundations, feature platforms, and policy-as-code clear that tax and free teams to ship. The next step is agentic: systems that manage themselves, with people setting direction and guardrails.
As Sohag puts it, the future is practical. Fewer heroics, more reusable systems. If you build for reuse and reliability now, your team will move faster when it counts.
Upskill your team: if you're formalizing a data and GenAI roadmap across product roles, explore curated learning paths by role at Complete AI Training.
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