Data Engineering & ETL
Batch + streaming pipelines, dimensional modelling and lakehouse architectures engineered for analytics, ML and regulatory reporting.
12B+
Rows processed / day
99.97%
Pipeline reliability
< 90s
Streaming lag
Capabilities
What you get
- Streaming ETL on Kafka
- dbt-modelled databases on MySQL / MongoDB
- Data quality gates
- Lineage + catalog
Engineering stack
Battle-tested tech
- Airflow
- dbt
- Kafka
- MySQL
- MongoDB
- Redis
Big Data · ETL · Lakehouse
Pipelines that survive Mondays
Bronze → silver → gold lakehouse on Snowflake / Iceberg, fed by Kafka, modeled in dbt, instrumented with OpenLineage end-to-end.
Storage architecture
Object lake
S3 · Parquet · Iceberg · partitioned
Warehouse
Snowflake · Redshift · BigQuery
Feature store
Online + offline parity
Metadata
Glue · Unity Catalog · Atlan
Lineage
OpenLineage · Marquez
Quality
Great Expectations · Soda
SLAs
Medallion architecture
Bronze · silver · gold separation
dbt models, versioned
Tests, docs and lineage in CI
Exactly-once
Across stream and batch
Institutional Framework
Data Engineering methodology — exactly-once semantics
Data Discovery & Lineage ADRs
Senior data architect-led discovery capturing source-to-target mapping and lineage requirements.
Model-driven trunk delivery
dbt-led modeling, mandatory column-level lineage checks, and data-diff validation in CI.
Pipeline Observability
Every pipeline ships with freshness dashboards, volume tracking, and automated failure playbooks.
Quality gates, not vibes
Freshness checks and schema validation are mandatory CI gates for every data release.
Technical Specifications
What runs underneath
Data Architecture — Kafka-fed tables, dbt models with column-level lineage, MySQL and MongoDB, exactly-once semantics across stream and batch.
Processing model
Exactly-once stream + idempotent batch processing
Transport
Kafka streaming with Schema Registry
Freshness target
Streaming lag < 90s, Batch EOD < 4h
Compute
MySQL / MongoDB with dbt-led transformation
Security & Scalability
Data Security posture
Column-level Security
Fine-grained access control on sensitive columns (PII) and automated masking policies.
Data Governance
Full end-to-end lineage from source to dashboard with automated cataloging.
Resilient Retries
Backpressure-aware streaming, dead-letter queues, and idempotent batch re-runs.
Compliance Ready
GDPR/CCPA compliant data deletion (right to be forgotten) and audit logging for all access.
Delivery Architecture
How it ships — blueprint to production
A production-ready data layer with automated pipelines and full data governance.
Reference architecture
Client edge → API gateway → services → data plane
Cross-cutting · Observability · Security · CI/CD · IaC
Integration touchpoints
Sources
MySQL, MongoDB, SaaS APIs, Webhooks
Warehouse
MySQL, MongoDB
Platform
AWS / GCP / Azure landing zone
Observability
Datadog, Custom Quality Checks
Governance
OpenLineage
Orchestration
Airflow
Execution timeline
- 01
Week 0–2
Source Audit
Senior data architect captures schema, volume, and lineage requirements.
- 02
Week 2–6
Database Found.
MySQL/MongoDB setup, raw ingestion pipelines, and the first vertical dbt model.
- 03
Week 6–12
Iterative Modeling
Two-week sprints focused on reporting layers, data quality, and dashboards.
- 04
Week 12+
Hardening & Go-live
Historical backfills, performance tuning, runbooks, and production cutover.