Data Engineering Services

PS3G provides data engineering services for organizations of every size: the pipelines that move data out of your operational systems, the warehouse or lakehouse that holds it in a shape people can query, and the tests and monitoring that keep it trustworthy. Whether you are a founder with three SaaS tools and a growing export habit, an operations lead whose reporting lives in a dozen spreadsheets, or an IT director with a warehouse that no longer keeps up, the engineering is the same. The outcome is data that arrives on schedule, means what the business thinks it means, and is ready for reporting, analytics and AI.

Data engineering services: what we build and for whom

Data engineering is the work between your source systems and the reports, models and applications that need their data. We build and run that layer. The typical starting point is a company that has outgrown exports and copy-paste: numbers disagree between departments, a monthly report takes a week of manual work, or a new AI initiative has stalled because nobody can get clean training data.

We work with the cloud you already use. On Microsoft Azure that usually means Azure Data Factory, Synapse or Microsoft Fabric and Azure SQL; on AWS, Glue, Redshift and S3; on Google Cloud, BigQuery, Dataflow and Cloud Storage. Where a dedicated platform fits, we build on Snowflake or Databricks. We are a data engineering company that is vendor-neutral: we do not resell any of these platforms and we will tell you when a simpler option is enough.

What the service covers

Area	Typical work	What you receive
Data pipeline development	ETL and ELT pipelines from databases, SaaS APIs, files and event streams; batch and incremental loads; change data capture	Version-controlled pipeline code, schedules, alerting and run history
Data warehouse services	Dimensional and data-vault modeling; warehouse build on Azure SQL, Synapse, Fabric, Redshift, BigQuery or Snowflake	A documented model, loaded history and a semantic layer for BI tools
Lakehouse and big data	Delta Lake or Iceberg tables on Databricks, Fabric or open-source Spark; medallion (bronze, silver, gold) layering	A lakehouse with defined zones, access rules and compute budgets
Data integration services	Two-way sync between CRM, ERP, e-commerce, support and finance systems; API and file-based integration	Integrations with mapping documents, error queues and retry logic
Data quality and testing	Validation rules, reconciliation against source totals, freshness checks, dbt or Great Expectations tests	A test suite that runs with every load and a quality dashboard
Streaming and real-time	Kafka, Event Hubs, Kinesis or Pub/Sub pipelines for events that cannot wait for a nightly batch	Streaming jobs with replay, dead-letter handling and monitoring
Migration and modernization	Moving from SSIS, Informatica, DataStage or hand-written scripts to cloud-native pipelines; warehouse migration to Snowflake, Databricks or Fabric	A migration plan, parallel-run results and a decommission checklist
Platform operations	Orchestration with Airflow, Data Factory or Dagster; cost monitoring; access control; backup and recovery	Runbooks, dashboards and an SLA-backed support arrangement if you want one

ETL services and ELT services are both on the list because the right pattern depends on where transformation is cheapest to run.

How we work

Process

1. Discovery. We list your sources, the questions the business wants answered, the people who own each dataset, and the current pain points. For a founder this is a two-hour conversation; for a larger organization it is a short series of workshops.
2. Architecture and plan. A written design: platform choice, modeling approach, pipeline patterns, security, cost estimate for the cloud spend, and a phased plan that delivers something usable early.
3. Build in increments. We deliver one source-to-dashboard path first, then widen. Every pipeline is in version control, reviewed and tested before it runs on a schedule.
4. Validate. Reconciliation against the source systems and sign-off from the data owners. Numbers that do not match are investigated, not explained away.
5. Hand over or run. Documentation, training for your team, and either a clean handover or a managed-services arrangement where we keep the platform running.

Team

A typical team is a data architect, one or more data engineers, and a QA engineer from our testing center of excellence who writes the reconciliation and regression tests. Larger builds add a platform engineer for infrastructure as code and a BI developer for the semantic layer. Engineers are in Wilmington, Delaware and in our India delivery center, which lets pipelines be watched around the clock.

Stack choices

We recommend based on what you already run and who will maintain the platform after we leave. A Microsoft-centered company usually lands on Fabric or Azure Data Factory with Azure SQL and Power BI. A company with heavy analytics and data science needs often chooses Databricks. Snowflake consulting comes up when the priority is a shared warehouse that many teams and tools query without managing infrastructure. Databricks consulting comes up when Spark workloads, machine learning and streaming sit alongside the warehouse. Open-source options (PostgreSQL, Airflow, dbt, Spark) are on the table when license cost matters more than managed convenience.

Ask for a fixed-scope quote on your first pipeline or warehouse phase.

Where this fits with the rest of your systems

Data engineering is the foundation under three other things we do. Data analytics and BI sits on top of the warehouse: dashboards, metric definitions and self-service reporting, including Power BI consulting for teams on the Microsoft stack. Big data solutions extend the same platform to high-volume and machine-learning workloads. And data governance defines who owns each dataset, how it is classified and how quality is measured, which the pipelines then enforce.

The warehouse also feeds systems that are not reports: a CRM, a pricing engine, a recommendation model or an AI assistant that must answer from current data. We design the platform so those consumers get the same governed data as the dashboards.

Examples by organization size

• A founder-led company with a web application, Stripe, a support desk and a marketing tool. We build a small warehouse on BigQuery or Azure SQL, a handful of pipelines, and one dashboard that shows revenue, churn and support load from one source of truth. Fixed scope, a few weeks of work, documented so the first in-house hire can take it over.
• A growing company with an ERP, a CRM, an e-commerce platform and a warehouse built by a contractor who has left. We assess what exists, rebuild the fragile parts as tested pipelines, add a semantic layer, and run the platform under a monthly arrangement while the company hires its own data engineer.
• A department inside a larger organization that needs its own data mart inside corporate security and procurement rules. We work with the central IT team’s platform (often Databricks or Snowflake), follow their access and naming standards, and deliver a mart the department owns, with lineage back to the enterprise sources.

The engineering patterns are the same in all three. What changes is the number of sources, the governance overhead and how the engagement is scoped.

Engagement models

• Fixed-scope project. A defined set of pipelines, a warehouse phase or a migration, with a written deliverable list and a fixed quote.
• Dedicated team or staff augmentation. Data engineers inside your team on a monthly basis, scaled up or down as the backlog changes.
• Managed services. We run and monitor the data platform under defined SLAs, handle failures, and add sources as you need them.
• Advisory. Platform selection, architecture review, data warehouse consulting, or a fractional head of data for a company not ready to hire one.

How we start: a first conversation, a short assessment, a written proposal.

Why PS3G for data engineering

We have been building data platforms for more than ten years, from DataStage and Informatica warehouses to today’s cloud lakehouses. Our engineers are certified on Microsoft and open-source technologies, and the team spans a US headquarters in Wilmington, Delaware and an India delivery center. We also build and run our own products: our HIPAA-compliant EHR and telehealth platform, DocVilla, runs on a distributed NoSQL database and carries regulated clinical data every day, so the data-quality and access-control habits on your project come from operating a platform, not just advising on one. Our testing center of excellence writes the reconciliation tests that catch silent data errors before your dashboards show them.

Related services

• Data analytics and BI solutions: dashboards and reporting on the warehouse we build.
• Big data solutions: Spark, streaming and machine-learning workloads at volume.
• Data governance: ownership, classification and quality rules the pipelines enforce.
• Power BI consulting: semantic models and dashboards for Microsoft-stack teams.
• Database development and migration: the operational databases that feed the pipelines.
• All services: everything we build, modernize and run.

Frequently asked questions

What is the difference between ETL and ELT, and which do you use?

ETL transforms data before loading it into the target; ELT loads raw data first and transforms inside the warehouse. We use ELT on modern platforms such as Snowflake, BigQuery, Databricks and Fabric because compute there is elastic and SQL-based transformation with dbt is easy to test and version. ETL still fits when the target is a legacy database or when sensitive fields must be masked before they land anywhere.

Do we need a data warehouse, or is a database enough?

A reporting database is enough when you have one or two sources, modest volume and a small group of users. You need a warehouse or lakehouse when sources multiply, when history must be kept beyond what source systems retain, when several tools query the same data, or when nightly loads start to slow the operational database. We will say which you need after a short assessment, and sometimes the answer is to wait.

Snowflake or Databricks: how do you choose?

Snowflake is the easier fit when the priority is a shared SQL warehouse that many teams query with little administration. Databricks fits when Spark workloads, data science and streaming sit alongside the warehouse and the team is comfortable with notebooks and clusters. Microsoft Fabric is the third option for Microsoft-centered organizations. We have no resale relationship with any of them, so the recommendation follows your workload and your team.

Can you migrate our SSIS or Informatica pipelines to the cloud?

Yes. We inventory the existing jobs, classify them (rebuild, lift as is, or retire), and rebuild the ones worth keeping as cloud-native pipelines in Data Factory, Glue, Dataflow or dbt. Each migrated pipeline runs in parallel with the old one until reconciliation matches, and only then is the old job switched off. The deliverable includes a decommission checklist so nothing is left running by accident.

How do you keep the data accurate?

Every pipeline carries tests: row counts and totals reconciled to the source, schema checks, freshness thresholds and business rules such as no negative quantities. Failures stop the load and alert a person rather than publishing bad numbers. We also record lineage so an analyst can trace a figure on a dashboard back to the source table and the transformation that produced it.