Two dashboards, two answers
Finance and sales each report a different revenue figure, and neither matches the billing system. Meetings turn into arguments about which number is right, and decisions wait until someone settles it.
Data Platform Engineering Services
For teams whose data platform keeps breaking. When two reports disagree, the cause is usually a pipeline or environment nobody owns. DharmOps diagnoses the cause first, then builds the platform and the pipelines on it across cloud, on-premises and hybrid environments, tested, monitored and documented, and stays on to run and support them.
An engineer replies within 24 hours. Or describe the problem first, and see our data platform case studies.
What It Looks Like When the Data Platform Is the Problem
Most teams don't call it a platform problem at first. They notice a symptom.
The job ran, but the number is wrong
A source system renames a field and a column fills with empty values. Nothing alerts, because most alerts watch for failed jobs, not wrong data. Someone finds it later in a report.
Every environment is different
Development, staging and production were set up by hand at different times. A pipeline that works in one breaks in the next, and nobody can rebuild an environment from scratch.
Cloud, on-premises and nothing in between
Some data sits in your own data centre, some in the cloud, and moving it between the two is a script someone wrote years ago. Residency rules decide where data can live, but the platform wasn't designed around them.
One person knows how it works
The platform runs because one engineer knows where everything is. The architecture diagram is out of date and the process isn't written down. If that person leaves, the knowledge leaves with them.
Engineers spend the week on fixes
The team retries failed jobs, patches the last break and resizes clusters by hand. New work waits behind the repairs.
Data Platform Engineering at DharmOps
Data platform engineering is designing, building and running the platform your data lives on: the environments, storage, processing and pipelines that move data from your source systems (CRM, ERP, billing, product databases, spreadsheets) into a warehouse, lake or lakehouse that reports and analysis read from. Data engineering services are part of it, alongside the infrastructure that keeps them running. We build on AWS, Azure and Google Cloud, in your own data centre, or across both.
Platform Architecture & Consulting
- Existing platform assessment and technical discovery
- Requirements, workload and capacity assessment
- Target architecture for cloud, on-premises or hybrid
- Technology and platform selection
- Source, destination and dependency mapping
- Batch or streaming architecture choice
- Proof-of-concept validation and implementation roadmap
- Architecture decision records
Cloud, On-Premises & Hybrid Platform Engineering
- Cloud data platforms on AWS, Azure and Google Cloud
- On-premises platforms in your own data centre: compute, storage and processing clusters
- Self-hosted Spark, Kafka and Airflow
- Hybrid connectivity, data synchronization and workload placement
- Multi-cloud where workloads need two or more providers
- Residency-driven architecture
- Development, staging and production environments
- Private networking and identity at the platform layer
Data Engineering & Pipeline Development
- Batch, incremental, API, file and database ingestion
- Change data capture (CDC) and event-based ingestion
- SaaS and enterprise connectors, plus custom connectors
- ETL and ELT pipeline development
- Cleansing, deduplication, enrichment and business rules
- Backfills, retries and schema evolution
- Source-to-target mappings and integration contracts
dbt for Data Engineering: A Practical Guide to Building Reliable ELT Pipelines
Warehouses, Lakes & Lakehouses
- Data warehouse architecture on Snowflake, BigQuery, Redshift or Azure Synapse
- Fact and dimension modelling, star and snowflake schemas
- Slowly changing dimensions and historical data
- Data lake storage layout and partitioning
- Raw, cleaned and curated layers
- Lakehouse architecture on Apache Iceberg, Delta Lake and Parquet
Data Processing, Orchestration & Automation
- Spark and distributed compute environments
- Streaming infrastructure: Kafka brokers, connectors and schema registry
- Orchestration with Airflow, Dagster or Prefect
- Infrastructure as code with Terraform or OpenTofu
- CI/CD with GitHub Actions or GitLab for platform and pipeline changes
- Environment promotion, drift detection and rollback
Platform Security, Quality & Observability
- Role-based access, service identities and least privilege
- Secrets management, encryption and audit logging
- Schema, completeness and referential checks on every pipeline
- Source-to-target reconciliation and data contracts
- Technical lineage and catalogue with DataHub, OpenMetadata or OpenLineage
- Health, utilization and job dashboards with Prometheus, Grafana and Alertmanager alert routing
Platform Migration & Modernization Delivery
- On-premises to cloud migration
- Cloud to cloud migration
- Re-platforming and component replacement
- Pipeline, storage and processing compatibility checks
- Parallel runs, cutover and rollback plans
- Post-migration handover
Platform Operations, Optimization & Support
- Workload profiling and bottleneck identification
- Capacity planning, scaling and workload isolation
- High availability, failover and disaster recovery for platform components
- Runbooks, documentation and knowledge transfer
- Upgrade planning
- Ongoing support from the engineers who built it
Who It's For
Companies that already depend on data, from growth-stage to mid-market, whose pipelines keep breaking, whose environments drift apart, or whose data is split across cloud and on-premises systems, and who have no one with the time to own fixing it. If your team is still running manual exports and has no warehouse yet, you may not need this yet.
What It Solves
Numbers that disagree, pipelines that fail without warning, environments that can't be rebuilt, reports that arrive late, and knowledge that lives in one person's head.
How It Starts
Our data engineering consulting begins with a diagnostic, not a tool or a quote. The diagnostic decides whether the platform runs in the cloud, on-premises or both.
What It Isn't
The data platform sits underneath dashboards and models. It doesn't include designing dashboards, building BI reports, training machine-learning models, RAG, vector search or customer data platforms. Database query tuning sits with Database Optimization, governance programs with Data Governance & Security, running AI in production with AI Operations, and spend reduction with Data Cost Optimization.
Where It Sits in Your Stack
The data platform is the layer between the systems that create data and everything that reads it: dashboards, finance reports, analysts and models. When a number is late or wrong, the cause is usually in this layer, not in the dashboard that shows it.
What Changes When the Data Platform Is Fixed
| What we put in place | What it means for you |
|---|---|
| A target architecture agreed before anything is built | Tool and platform choices follow from your workloads, not from a vendor pitch |
| Environments defined as code, with CI/CD for every change | Development, staging and production match, and any environment can be rebuilt |
| One transformation step in the pipeline, so data is cleaned and combined once | Every report reads from the same prepared data |
| Freshness, volume and schema checks on every pipeline, with alerts | Wrong or missing data is caught by an alert, not by someone in a meeting |
| Secure connectivity between cloud and on-premises systems | Data moves where it needs to and stays where residency rules say it must |
| Runbooks and documentation written for the people who will run it | One person leaving doesn't stop the work |
What You Receive and What You Provide
What You Receive
Diagnostic output
A current-state assessment of how data moves from your source systems to the reports and decisions that depend on it, with a written plan that ranks fixes by impact and effort and says whether to fix what exists or rebuild.
Target architecture
The platform design for cloud, on-premises or hybrid: storage, processing, networking, environments and technology choices, agreed with you before anything is built.
Platform environments
Development, staging and production environments provisioned with infrastructure as code, so each one can be rebuilt and changes go through CI/CD.
Ingestion and pipelines
Connectors, ingestion jobs and ETL/ELT pipelines with source-to-target mappings, built with freshness, volume and schema tests live from day one.
Warehouse, lake or lakehouse
The storage layer and data model your reports and analysts read from, organized into raw, cleaned and curated layers.
Security and monitoring
Access controls, secrets, encryption and audit logging at the platform layer, plus alerts for late data, missing data, failed jobs and capacity limits.
Runbooks
Step-by-step procedures for deployments, rollbacks, known failure modes and recovery, written so a junior engineer could follow them alone at 2am.
Documentation and training
Architecture, configuration and every pipeline documented with the reasoning behind each change, and sessions for the people who will run the platform.
Ownership
The code, infrastructure definitions, credentials and documentation belong to you. Nothing proprietary sits in the middle.
What You Provide
- Access to your source systems, existing platform and the cloud accounts or data-centre environments involved, approved by you before we use it.
- People who know the data: someone who can explain how it is produced and someone who can explain how it is used.
- Agreement on the source of truth. Your team decides which system holds the correct version of each kind of data, and we build the pipelines around that.
- Infrastructure and security contacts who can approve network, identity and firewall changes in your environments.
- A list of what you have today: core systems, where they run, what is held in spreadsheets, and whether a warehouse or lake already exists.
- Early notice of regulated data such as health, financial or personal data, and any residency rules, so handling is agreed before work starts.
How an Engagement Works
Six steps, in order. Each ends with something you can see, and nothing moves on until you've approved it.
- Discovery call
- Diagnose
- Prioritize
- Fix
- Stay
Discovery Call
We talk through what you're seeing, what you have today and what you need.
- You receive: a clear answer on whether data platform engineering is the right next step
- You provide: a short description of the symptoms, the systems involved and where they run
- Approval: both sides agree there is something to diagnose
Diagnostic
The step after the discovery call, and the gate before any scope is set. We interview the people who use and maintain the data, review the current architecture, inventory the sources and environments, and map how data moves from source to report, including the redundancies and the gaps.
- You receive: a written plan ranking fixes by impact and effort, a fix-or-rebuild recommendation, and the cost and risk behind each option
- You provide: read access to the main source systems and platform, and time with people who know how the data is produced and used
- Approval: you approve the plan
Scope is quoted after this step, not before
Design
We design the target platform: architecture, environments, pipelines, storage and data model.
- You receive: a design document covering the cloud, on-premises or hybrid architecture, the pipelines, the warehouse or lakehouse structure and the checks that will run
- You provide: agreement on the source of truth for each kind of data, and sign-off from your infrastructure and security contacts
- Approval: you sign off on the design before any build starts
Build
We provision the environments as code and build the pipelines with freshness, volume and schema checks in from the start. Every change is documented with its reasoning.
- You receive: a platform running in your environment, regular demos of progress and documentation written as we go
- You provide: a named contact who answers questions promptly, and a decision on any source-system issue we flag
- Approval: each component is accepted against criteria agreed in the design
Cut over
The new platform runs under real load next to the old one. We watch the alerts, fix what appears and reconcile the numbers, with a rollback plan ready.
- You receive: reconciled figures, resolved issues and a log of what was fixed
- You provide: someone on your side to confirm the numbers match what the business expects
- Approval: the numbers reconcile to the source and you approve retiring the old path
Ongoing support
Support starts at cut over, with the same engineers who built the platform. The first weeks get closer attention, because that is when production surprises appear.
- You receive: fixes when a pipeline breaks, adjustments when a source changes, capacity and upgrade planning, small changes handled as requests, and runbooks that stay current
- You provide: a named contact, and early notice when a source system, tool or environment is about to change
- Approval: support scope is agreed in writing before support begins
Continues after cut over, with the same engineers
Timeline
How Timing Works
The diagnostic produces a timeline with milestones, each tied to something you can verify, not only a date. Dates move when a dependency on either side moves, and we tell you the same day it happens. The dependencies that matter most:
- Access: how quickly access to systems and environments is approved.
- People: how available the people who know the data are.
- Source of truth: how quickly the source of truth for each dataset is agreed.
- Sources: how many sources are involved and what state they are in.
- Environments: whether the platform is cloud, on-premises or hybrid, and how long network and infrastructure changes take on your side.
- Review: compliance or security review, if your data is regulated.
Two Data Platform Engineering Engagements
Logistics: manual billing to an automated platform
- Situation
- A logistics operator's billing ran as a manual process.
- What we built
- An automated data platform on PostgreSQL, Python, Airflow and Superset, processing 2M+ records a day.
- Result
- Manual effort dropped sharply, and data issues that used to surface after days are caught close to real time.
E-commerce: one-off pipelines to self-service
- Situation
- Custom, one-off pipelines, with requests queuing at a central data team.
- What we did
- Assessment, design, pilot, documentation and expansion on a Snowflake platform.
- Result
- A repeatable platform where problems are caught earlier.
"DharmOps took the time to genuinely understand what we needed, not just the technical spec, but the underlying problem."
More on our work in all case studies.
Why Teams Bring DharmOps In
We diagnose before we recommend.Tool-first pitches and quotes made before anyone has looked at the data are common in this field. We start with the diagnostic, and the written plan comes before any tool, timeline or price.
Cloud, on-premises or hybrid.We build where your data has to live: AWS, Azure, Google Cloud, your own data centre, or across them. On-premises is a design choice we engineer for, not only something we migrate you away from.
We know the databases underneath.Many platform problems start in a database that is slow, misconfigured or poorly modeled. Our engineers work hands-on across PostgreSQL, Oracle, SQL Server, MySQL and MongoDB as well as Snowflake, BigQuery and Databricks, so those causes are found, not routed around.
You own it, and we stay.Code, infrastructure definitions, credentials and documentation are yours. After launch, DharmOps stays on to run and support what was built, so the first break isn't left to chance.
How We Handle Your Systems
Giving an outside team access to your source systems is a serious decision. These are the rules we work by.
- NDA before any access. Signed before we see anything.
- Least access. We ask only for what the current step needs.
- Approval before access. You approve each access level before we use it.
- Your data stays where it lives. We work inside your environment, in the cloud or on-premises.
- Deletion we verify. When work ends, anything we held is deleted and the deletion is confirmed.
- Written incident procedure. If something goes wrong, there is a documented process, not improvisation.
Questions Teams Ask Before Booking
What is data platform engineering?
Data platform engineering is designing, building and running the platform your data lives on: the environments, storage, processing, orchestration and pipelines that move data from source systems into a warehouse, lake or lakehouse. It includes data engineering, plus the infrastructure, automation, security and monitoring that keep those pipelines running.
How is it different from data engineering services?
Data engineering services usually mean building pipelines and data models. Data platform engineering covers that work and the platform underneath it: environments, compute, networking, infrastructure as code and CI/CD. If the platform itself is part of the problem, such as environments that drift or clusters sized by hand, you need both.
Do you build on-premises and hybrid platforms, or only cloud?
All three. We build cloud data platforms on AWS, Azure and Google Cloud, on-premises platforms in your own data centre, and hybrid platforms with secure connectivity and data synchronization between them. Where the data lives is decided by your workloads, cost and residency rules, and the diagnostic recommends the model before anything is built.
Do we need a rebuild, or can our current platform be fixed?
The diagnostic answers this before anything is built. Its written plan says which parts of the platform and which pipelines are worth keeping, which should be replaced and why, with the cost and risk of each option. A rebuild is recommended only when the findings show a fix won't hold.
Should we hire a data engineer instead of a data engineering company?
That depends on how much ongoing work there is. A full-time hire is the right choice when the work is steady and you have someone to manage it, but a senior hire can take months to find, and one person holding all the knowledge is the risk you're trying to remove. DharmOps builds the platform and stays on to run it, and the runbooks and documentation mean a future hire can take over without starting from zero.
Can't we just use a tool like Fivetran or Airbyte?
Often a tool is part of the answer. Tools move data from one place to another, but they don't decide which system is the source of truth, design the environments, add the checks that catch wrong data, or give anyone ownership of the platform when it breaks. The diagnostic recommends tools only after looking at your data, and you own the code, so the stack can change later.
How is our data kept confidential?
We sign an NDA before any access. Access is the minimum required, you approve it before we use it, and your data stays where it lives, in the cloud or on-premises. Deletion is verified when the work ends, and a written incident procedure is in place. If your data is regulated, such as health, financial or personal data, tell us on the first call so handling is agreed before work starts.
How long does it take?
It depends on the number of sources, the state of the data, the environments involved and whether this is a new build or a repair of what exists. The diagnostic gives you a scoped plan with a timeline before you commit to anything. A scoped first project, such as stabilizing the pipelines that fail most often, is a workable place to start. For reference, the logistics platform ran roughly 18–20 months.
How is data platform engineering priced?
Scope is quoted after the diagnostic, once we have looked at your systems, and before you commit. Cost depends on the number of sources, the volume and condition of the data, cloud, on-premises or hybrid delivery, whether this is a new build or a repair, compliance requirements and whether support continues afterward. A fixed price given before anyone has looked at the data is a guess.
What happens after the build?
The same engineers who built the platform stay on it. They fix breaks, adjust pipelines when a source changes, plan capacity and upgrades, and handle small changes as requests. The runbooks and documentation stay current, so the platform doesn't depend on one person. A new set of sources or a new data domain is quoted as a new project.
Data Platform Engineering Use Cases by Industry
Every industry has source systems that don't talk to each other, often split across cloud and on-premises environments. Data platform engineering is the platform and pipeline work that turns them into one dataset your teams can query.
Data Platform Engineering for Retail & eCommerce
POS, storefront, and warehouse systems each hold part of the inventory picture. We build ELT pipelines into one warehouse so stock, sales, and demand sit in one place.
Data Platform Engineering for SaaS & Software
Product usage events have to reach reporting without a ticket to the data team. We build the ingestion, dbt models, and self-service golden paths that let product and finance teams ship their own datasets.
Data Platform Engineering for Logistics
Load events, warehouse scans, telematics, and EDI documents arrive in different formats. We land each source raw, normalize it in a silver layer, and publish business-ready views on top.
Data Platform Engineering for Healthcare
Claims, EHR extracts, and lab feeds use different codes and identifiers. We build pipelines that standardize them and enforce data contracts, so downstream reporting stops breaking when a feed changes.
Data Platform Engineering for FinTech
Ledger and payment data have to reconcile across systems. We build the pipelines with schema checks at each hop, so a changed field is caught at ingestion instead of in a finance report.
Data Platform Engineering for Manufacturing
MES, ERP, and sensor data live in separate systems on separate schedules. We integrate them into one platform, so plant and finance teams read the same numbers.
Often Paired With
Data Reliability
We build the platform with baseline checks; Data Reliability owns ongoing data SLOs, incident response and recovery across the wider data estate.
Real-Time Data Engineering
We provision streaming infrastructure; Real-Time Data Engineering builds the event processing and low-latency pipelines on top of it.
Data Modernization
We deliver the platform work; Data Modernization leads when the main need is a strategy and roadmap for replacing a legacy estate.
Database Optimization
We connect the platform to your databases; Database Optimization handles query plans, indexes, replication and engine tuning.
See all services.
Start With a Conversation About Your Data Platform
If your reports disagree, your pipelines break without warning, your environments drift apart or only one person understands the platform, the first step is a 30-minute discovery call.