Data Pipelines That Don't Silently Break
We build ingestion, transformation, and warehouse pipelines that you can actually trust on a Monday morning. Idempotent loads, schema contracts, freshness SLAs, lineage you can read, and alerts that fire before the CEO asks why the dashboard is empty. No overnight cron jobs that fail in silence. No notebooks held together with hope.
You don't need another dashboard.
You need data you can trust.
Most data teams aren't drowning in tools — they're drowning in pipelines that broke last Tuesday and nobody noticed until Friday's board meeting. The dashboard says revenue is up 4%. Actually, the Stripe webhook has been failing for three days, the Salesforce sync is loading duplicates, and the last successful dbt run was on the 14th. We build data infrastructure where the failure mode is loud and the success mode is automatic.
The pipeline that fails in silence
A cron job runs at 2am, the API returns a 502, the script swallows the exception, the table is empty, the dashboard renders zero rows, and nobody finds out until the Monday standup. The bug nobody owns until it costs a deal.
The "data swamp" warehouse
Three versions of "customer", five definitions of "active user", a table called dim_users_v2_FINAL_use_this, and zero documentation. Every analyst writes their own SQL and gets a different answer to the same question.
Loads that double-count when you retry
The pipeline failed halfway. You re-ran it. Now half the rows are in twice. Revenue is overstated, the CEO presents the wrong number, and you spend two days writing the deduplication script that should have been an idempotent insert from day one.
What You Actually Get
No vague deliverables. Here's exactly what lands in your hands.
Idempotent ingestion you can re-run
Every load is keyed, every retry is safe, every failure recovers from where it stopped. Backfill a year of data without breaking today's tables.
A modeled warehouse with contracts
dbt models with tests, documentation, and column-level descriptions. Staging → intermediate → marts. One source of truth for "customer", one for "revenue", one for "active user" — defined in code, not in a Google Doc.
Freshness, volume, and quality alerts
If a table is stale, an SLA is missed, or row counts move 3 sigma off baseline — Slack gets pinged, on-call gets paged, and the dashboard shows a "stale data" banner. Failures are loud, not invisible.
Lineage and observability you can read
Every column traceable from source system to final dashboard. When marketing asks "where does this number come from?", the answer is one click away — not a Slack archaeology project.
A Real Data Engineering Team
Building reliable data infrastructure takes more than a dbt enthusiast. Six roles you get on every Pillai Infotech data build.
Data Architect
Picks the warehouse (Snowflake / BigQuery / Redshift / Databricks), designs the layer model, and decides what's a dimension, what's a fact, and what's a slowly-changing mess. The decisions that determine whether year two is queries or rewrites.
Senior Data Engineer
Owns ingestion, orchestration, and transformations. Knows when Airflow wins over Dagster, when Fivetran wins over a custom connector, and when "just put it in S3 first" is the right answer.
Analytics Engineer
Lives in dbt. Builds the staging → marts layer, writes the tests, owns the metrics layer, and says no when someone wants to bypass the model and query raw. The hire most teams skip and regret.
Data Reliability Engineer
Sets freshness SLAs, instruments monitoring (Monte Carlo, Elementary, Soda, or homegrown), and runs the on-call rotation. The person who makes sure failures are loud.
Data Governance Lead
PII classification, row-level security, masking, GDPR / DPDP / HIPAA scope, retention policies, and the access review your auditor wants. Files the paperwork so the CDO doesn't lose sleep.
FinOps Engineer
Watches the warehouse bill. Tunes warehouses, kills runaway queries, sets autosuspend, partitions and clusters big tables. The person who turns a $20k Snowflake bill into a $6k one without anyone noticing in performance.
You See Everything. In Real Time.
Every Pillai Infotech project comes with a dedicated client dashboard. Kanban boards, live logs, test results, meeting notes — it's all visible the moment it happens. No status-report theatre, no "we'll get back to you", no surprises at the demo. You work with us like you work with your own team.
Kanban Board, Live
Every epic, every story, every task — visible on your dashboard. Drag, comment, reprioritize. It's the same board our team works from.
Documented Everything
Every decision, spec, API contract, and architecture diagram lives in the dashboard. Searchable, versioned, linked to the tasks they shaped.
Live Logs & Test Results
Build logs, deployment logs, test suite results — streamed to your dashboard the moment they run. You never have to ask "did the build pass?"
Meetings → Tasks, Automatically
Every meeting is recorded, transcribed, and every action point is auto-converted into a tracked task assigned to the right person. Nothing gets lost between calls.
Sprint Burndown & Velocity
See exactly how much work is done, how much remains, and our velocity over time. If a sprint is slipping, you see it the same moment we do.
Comment, Approve, Decide — In-Place
Comment on any task, approve designs, sign off on specs, and raise blockers directly in the dashboard. Everything tied to the work, not buried in email threads.
Data Systems We Know How to Ship
We pick the architecture to match the questions your business actually asks, not the conference talk we saw last week.
🏢 Modern data stack warehouses
Fivetran / Airbyte / custom into Snowflake / BigQuery / Redshift, modeled in dbt, served to Looker / Metabase / Tableau. The boring, proven stack that scales from 10GB to 10TB.
🌊 Streaming & real-time pipelines
Kafka, Kinesis, Pub/Sub into ClickHouse, Pinot, or Materialize. For dashboards measured in seconds, not hours. We'll tell you honestly when batch is fine and streaming is a vanity project.
🔄 Reverse ETL & operational analytics
Warehouse → Salesforce, HubSpot, Braze, Customer.io. Hightouch or Census wired up so the data team's models actually drive the business, not just decorate slides.
🤖 ML & feature pipelines
Feature stores (Feast, Tecton, or homegrown), training data pipelines, prediction serving, and the offline-online consistency checks that prevent training-serving skew.
🏛️ Lakehouse architectures
Iceberg, Delta Lake, or Hudi on S3 / GCS, queried from Trino, Athena, or Databricks. For petabyte data, mixed workloads, or escaping a vendor lock-in problem.
📡 Embedded & customer-facing analytics
Multi-tenant warehouses with row-level security and sub-second query targets, embedded into your SaaS product. The hard parts: tenant isolation, query cost control, and caching.
The Data Stack We Use
Boring, proven, and chosen because pipelines run, not because the vendor sponsored the conference.
Warehouses & Lakehouses
Ingestion & Orchestration
Transformation & Modeling
Observability & BI
A Six-Stage Data Delivery Process
Built around the reality that the second source system always breaks the assumptions made for the first.
Discovery & Question Audit
What questions does the business actually need answered, who asks them, how often, and what's the cost of being wrong. We design backwards from the question, not forwards from the source.
Source & Schema Mapping
Every source system audited: API, schema, rate limits, change tracking, PII fields, and historical depth. Documented in writing before a single connector is configured.
Build in Vertical Slices
One source → staging → marts → dashboard, end-to-end, every two weeks. Real data, real tests, real freshness alerts. No "we'll add monitoring later".
Tests, Contracts & SLAs
Schema contracts on every source. dbt tests on every model. Freshness, volume, and quality SLAs in writing. The failure modes designed before they happen.
Cutover & Documentation
Old pipelines retired, new pipelines documented in lineage, runbooks for every alert, and a handover session with the team that will own this on Monday.
Reliability & FinOps
Monthly review of pipeline failures, SLA breaches, query costs, and warehouse spend. We catch the runaway query before it becomes a bill, and the broken source before it becomes a board slide.
Three Ways to Engage
Data projects don't fit one shape. Pick the one that matches your stage.
Data Reliability Audit
Two-week deep dive on your existing pipelines: failures, freshness, duplication, cost, and gaps. You get a prioritized fix list with effort estimates.
- Pipeline + warehouse audit
- Reliability + cost report
- Honest rebuild-vs-fix recommendation
Fixed-Scope Data Build
End-to-end data platform build from source systems to first trusted dashboard, with monitoring, lineage, and a 60-day warranty.
- Fixed scope, fixed price
- Typical: 8–16 weeks
- 60-day post-launch warranty
Embedded Data Squad
A dedicated data engineer + analytics engineer + reliability engineer working alongside your team on a continuous release cycle.
- DE + AE + Reliability + PM
- Monthly retainer, scale up/down
- Best for: ongoing data platform growth
Honest Answers to Data Reality Questions
The questions every smart buyer asks before signing. Here's what we tell them.
Snowflake, BigQuery, Redshift, or Databricks?
Snowflake if you want the most ergonomic platform and you can pay for it. BigQuery if you're already on GCP or your workload is bursty (pay per query wins). Redshift if you're deep in AWS and want predictable cost. Databricks if you're doing serious ML and need notebooks + warehouse + lakehouse in one. We'll match the warehouse to your workload, not to a partner badge.
Do we need streaming or is batch fine?
Batch is fine for 90% of dashboards, including most "real-time" requests. Streaming earns its complexity when the business genuinely loses money on stale data — fraud detection, ad bidding, in-game telemetry, fulfillment SLAs. We'll ask what decisions get made on the data and how often, and tell you honestly. Most "real-time" requests are satisfied by hourly batch.
Fivetran or build our own connectors?
Fivetran / Airbyte for any standard SaaS source — Salesforce, HubSpot, Stripe, NetSuite, Postgres replicas. The math almost always wins vs maintaining your own. Custom connectors only for proprietary internal systems or APIs Fivetran doesn't cover. We'll do the cost math with you.
How do you handle PII and GDPR / DPDP?
PII classification at ingestion, masking and tokenization for non-privileged roles, row-level security where needed, and a documented retention policy enforced by scheduled deletion jobs. We design the governance model in week one, not after the audit notice.
How do you stop the warehouse bill from running away?
Autosuspend on every warehouse. Query cost monitoring with per-user, per-team breakdown. Killed long-running queries, partitioning and clustering on big tables, and a monthly FinOps review. We've cut Snowflake bills by 50–70% on inherited warehouses without anyone noticing in performance.
Why dbt? Can we use stored procedures instead?
You can, and you'll regret it. dbt gives you version control, tests, lineage, documentation, and a model graph that survives team turnover. Stored procedures give you none of that. The exception is when you're inside a vendor that doesn't play well with dbt — and even then, SQLMesh is usually a better answer than procs.
How quickly will we know when a pipeline breaks?
Within 15 minutes. Freshness checks run on every key table, dbt tests run on every transformation, and any failure pages on-call via PagerDuty / Opsgenie / Slack. The dashboard shows a "stale data" banner so the business knows before they ask.
Can you migrate us off our current data stack?
Yes — Redshift to Snowflake, Looker to Metabase, Stitch to Fivetran, legacy ETL to dbt, whatever the move is. We dual-run the old and new pipelines, validate row-by-row, then cut over. Zero data loss, zero "we'll figure out the discrepancies later".
Who owns the warehouse, the pipelines, and the BI tool?
You do. Warehouse account in your name, code in your GitHub org, BI tool in your name, secrets in your vault. We work inside your environments. If we walked away tomorrow, your team could keep the pipelines running on Monday.
Can you sign an NDA before we share details?
Always. NDA before the first call. We're happy to work inside your tooling and your data clean room if compliance requires it.