[ Data Engineering ]
Numbers Your Whole Company Agrees On, Every Morning
Finance, sales and product each pull their own exports and get different answers. We build pipelines and a warehouse that turn scattered app, billing and marketing data into tested, documented models your dashboards and AI features can trust.
Finance$4.12M
Sales$4.37M
Product$3.96M
Dashboards are only as good as the pipelines behind them
Most reporting problems are not BI problems. Data arrives late, columns change without warning, one customer has three IDs, and metric definitions live in someone's head. A modern data stack moves raw data into a warehouse on a schedule, transforms it with tested SQL, and publishes a single set of definitions for revenue, churn and usage that every team uses.
What a healthy data platform checks every run
- One definition per metricRevenue, active users and churn are defined once in a semantic layer, so every dashboard shows the same number.pass
- Fresh data on a scheduleManaged connectors and orchestrated jobs load sources hourly or daily, with streaming where minutes matter.pass
- Tests catch bad dataFreshness, uniqueness and range checks run with every load and alert the owner before a broken number reaches a board deck.pass
- Ready for AIClean, documented and access-controlled tables are the starting point for forecasting, search and LLM features.pass
Data Engineering Services
From a first warehouse for a Series A startup to streaming pipelines across business units, scoped to the decisions the data needs to support.
01Sources and ingestion
Data Pipelines and Ingestion
Batch and incremental pipelines from product databases, SaaS tools, files and APIs, orchestrated with clear ownership and retries.
Fivetran or Airbyte connectors · Custom API extractors · Airflow or Dagster orchestration
Streaming and Real-Time Data
Kafka and change data capture pipelines for events, fraud signals, inventory and live product analytics.
Kafka or Kinesis topics · Debezium CDC · Stream processing jobs
02Storage
Cloud Data Warehouses
Snowflake, BigQuery or Redshift set up with sensible warehouse sizing, role-based access and cost monitoring.
Warehouse architecture · Role-based access model · Cost and usage monitors
Lakehouse Architecture
Open table formats on object storage for large or semi-structured data, queried by Databricks, Spark or the warehouse.
Delta Lake or Iceberg tables · Bronze, silver, gold layers · Catalog and governance
03Modeling and quality
ELT Modeling with dbt
Staging, intermediate and mart layers in dbt, with tests, documentation and CI that runs on every pull request.
Layered dbt project · Data tests and docs · dbt CI pipeline
Data Quality and Observability
Automated checks on freshness, volume, schema and values, with alerts routed to the team that owns each table.
Data contracts · Anomaly alerts · Lineage graph
Data Governance and Privacy
PII tagging, column masking, retention rules and access reviews that support GDPR, HIPAA and SOC 2 requirements.
PII classification · Masking policies · Access audit reports
04Serving
BI Dashboards and Semantic Layer
Executive, finance and product dashboards built on governed metrics, with self-service exploration for analysts.
Metrics layer · Looker, Power BI or Metabase · Embedded analytics option
AI-Ready Data Foundations
Feature tables, document pipelines and vector embeddings that feed forecasting models, RAG search and AI agents.
Feature store tables · Document chunking pipeline · Embedding sync jobs
First Trusted Dashboard Within Five Weeks
We start from the questions leadership asks most often and build backwards to the sources, so useful output arrives early.
- 01 · Week 1
Metrics and sources discovery
Workshops to list the key business questions, agree metric definitions and inventory every source system and owner.
- 02 · Weeks 2 to 3
Platform setup
Warehouse, ingestion tools, orchestration and environments provisioned in code, with access roles and cost controls.
- 03 · Weeks 3 to 5
Core models and first dashboard
dbt models for the highest-value domain, usually revenue or product usage, and the first dashboard leadership reviews weekly.
- 04 · Weeks 5 to 10
Domain expansion
Additional sources and marts for finance, marketing and operations, plus streaming where latency matters.
- 05 · Weeks 4 to 11
Quality, lineage and governance
Tests, freshness alerts, lineage and PII policies added as each model ships, not as a cleanup project at the end.
- 06 · Weeks 11 to 12
Enablement and handover
Analyst training, contribution guidelines for the dbt project and 30 days of hypercare after handover.
Your First Trusted Dashboard, Fast
We agree the metrics that matter, connect the sources behind them and ship one tested dashboard your leadership can rely on, before expanding domain by domain.
Plan the First Dashboard- Tested models
- dbt
- Tested models
- Pipeline code yours
- 100%
- Pipeline code yours
Why Data Teams Choose Sajal Tech
- TestedEvery modelEach dbt model ships with tests and documentation, so data issues surface in CI, not in a board meeting.
- ToolNeutral adviceWe pick Snowflake, BigQuery, Redshift or a lakehouse based on your data volume, team skills and budget.
- AIReady by designAs an OpenAI Select Partner and a Claude Select Services Partner, we structure data so it can power LLM search, agents and forecasting later.
- FixedPrice per phaseEach phase has an agreed scope and price, and warehouse running costs are estimated before you commit.
The Modern Data Stack We Use
Managed and open-source tools chosen for your data volume, latency needs and team skills.
- Snowflake
- BigQuery
- Redshift
- Databricks
- Delta Lake
- Apache Iceberg
Data Leaders We Have Worked With
Rated 4.9/5 on average across the platforms clients review us on.
Clutch5.0
GoodFirms5.0- Google5.0
- Upwork4.9
I had the pleasure of working with Sajal Tech on a travel website, and it was an incredible experience from start to finish. They demonstrated professionalism, clear understanding of requirements, and excellent communication throughout.
Data Engineering by Industry
- FinTechTransaction ledgers, risk features and regulatory reporting.
- Retail and eCommerceUnified orders, inventory and marketing attribution.
- Healthcare and WellnessClinical and operational analytics with PII controls.
- LogisticsShipment events, delivery times and route cost analysis.
- EnergyMeter and sensor time series at high volume.
- Media and EntertainmentViewer engagement, content performance and ad revenue.
What Data Engineering Costs
Indicative starting prices, excluding warehouse and tool subscriptions. A fixed quote follows a free 30-minute scoping call.
Analytics starter
A warehouse, core pipelines and the first set of dashboards for one team.
From$8,000
4 to 6 weeks
- Warehouse setup
- Up to five sources
- dbt core models
- Two dashboards
Company data platform
Most chosenMultiple domains, governed metrics and data quality monitoring across teams.
From$25,000
8 to 14 weeks
- Everything in Starter
- Semantic metrics layer
- Data quality alerts
- Access and PII policies
Streaming and AI data
Real-time pipelines, lakehouse storage and data foundations for ML and LLM features.
From$50,000
3 to 6 months
- Kafka or Kinesis streaming
- Lakehouse tables
- Feature and embedding pipelines
- SLA-backed support
Data Engineering Questions
BigQuery suits teams already on Google Cloud with spiky query patterns. Snowflake is strong for multi-cloud setups and data sharing, and Redshift fits AWS-heavy teams with steady workloads. We compare expected monthly cost for your actual data volume before recommending one.
An analytics starter with a warehouse, core pipelines and dashboards starts at $8,000. A company-wide data platform typically starts at $25,000, and streaming or AI data foundations at $50,000. Warehouse and connector subscriptions are billed separately by the vendors.
Usually by week four or five. We build the most important domain end to end first, then expand to other sources.
ETL transforms data before loading it into the warehouse. ELT loads raw data first and transforms it inside the warehouse with tools like dbt, which is cheaper to change and easier to test. Most modern stacks use ELT.
Only if a decision or feature depends on data that is minutes old, such as fraud checks, live inventory or in-app analytics. Hourly or daily batch loads are cheaper and cover most reporting needs.
Every model has tests for uniqueness, nulls, accepted values and relationships, and every source has freshness checks. Failures alert the table owner, and data contracts with source teams stop breaking schema changes before they ship.
Yes. We build clean feature tables for ML models and document pipelines that chunk, embed and sync content into a vector database for RAG search and AI agents, with the same access controls as the rest of the warehouse.
We set warehouse sizes, auto-suspend, query timeouts and budget alerts from the start, and use incremental models so large tables are not rebuilt on every run. You get a monthly cost report broken down by workload.
Your team can, using the documentation and contribution guide we provide. If you prefer, we offer monthly support plans covering pipeline monitoring, new sources and model changes.
Let's build your next product together
Book a free strategy call and leave with a clear plan and estimate. No commitment.
- 01Pick a time that suits you
- 0230 minutes on scope, stack, timeline and budget
- 03Fixed-price proposal, NDA on request