Data & Analytics
Data Engineering Services
Turn scattered operational data into trusted pipelines, modern reporting, and dashboards leaders can use every day.
What we deliver
We design and build the full data stack, from the pipelines that move your data to the dashboards your leadership actually trusts, drawing on hands-on delivery experience across healthcare, financial services, telecom, and logistics.
ETL / ELT Pipelines
Most businesses don't have a data problem, they have a data movement problem. Information lives in a CRM, a handful of spreadsheets, a SaaS billing tool, and an on-prem database that nobody wants to touch, and none of it talks to each other. We design and build ETL and ELT pipelines that pull data from cloud platforms, on-premises systems, and SaaS APIs, clean it, transform it into a consistent structure, and load it into a warehouse your team can actually query. We've built these pipelines using Azure Data Factory, SSIS, IICS (Informatica's cloud-native integration platform, useful when you need enterprise-grade connectors without managing your own integration servers), Python, and AWS Glue. On the receiving end, we work just as comfortably across Snowflake (for fast, scalable warehousing with minimal tuning), Microsoft Fabric (when a client is already standardized on the Microsoft ecosystem and wants a unified lake and warehouse in one place), and Google Cloud Platform, including BigQuery and Dataflow, as we do on AWS, so the destination platform is whichever one actually fits your stack, not whichever one we happen to default to. Across healthcare, finance, telecom, and logistics, we've already solved the edge cases most teams hit for the first time: malformed records, duplicate keys, schema drift, and late-arriving data. Every pipeline we build includes monitoring and alerting, so when a source system changes or a load fails, you find out from an alert, not from a confused executive asking why this week's dashboard looks wrong. The result is a steady, dependable flow of clean data into one place, instead of five disconnected systems that each tell a different story about the same business.
Power BI Reporting
A dashboard nobody trusts is worse than no dashboard at all. We build Power BI reporting layers that start with a proper semantic model, not just a pile of visuals stacked on raw tables, so the numbers stay consistent no matter who's looking or which report they open. That means defining your KPIs once, getting the relationships and DAX measures right, and setting up row-level security so the right people see the right data. We design executive dashboards for leadership, operational reports for day-to-day teams, and self-service models power users can explore without breaking anything underneath. We also handle the parts that get ignored until something breaks: refresh scheduling, gateway configuration for on-prem sources, version control for report changes, and a governance structure so reports don't quietly drift out of sync with the business. Having delivered this across healthcare, finance, telecom, and logistics clients, we've already dealt with compliance-sensitive data and the specific reporting quirks of regulated industries. The goal isn't a one-time impressive demo, it's a reporting system your team still trusts and still uses six months after we hand it off.
Cloud Data Platforms
Picking a cloud data platform is a long-term commitment, and the wrong choice shows up two years later as a painful, expensive migration. We design and build on BigQuery, Snowflake, Databricks, Microsoft Fabric, and AWS, choosing the platform based on your actual workload, query patterns, team skill set, and budget, not whichever vendor has the best sales deck. That includes architecting the warehouse layer, setting up compute and storage so costs don't spiral as data volume grows, configuring access controls and data sharing, and building the pipelines that feed the platform so it's never sitting empty waiting for data. We're equally comfortable building a lakehouse architecture on Databricks for a team running machine learning workloads, or a straightforward Snowflake warehouse for a finance team that just needs fast, reliable reporting. Because we've delivered cloud data platforms across healthcare, financial services, telecom, and logistics, we understand the compliance and data residency requirements that come with regulated industries, and we build with those constraints in mind from day one. Whether you're migrating off legacy infrastructure or building cloud-native from scratch, we handle the architecture, the migration, and the documentation, so your team understands exactly what's running and why.
Data Quality
Bad data is expensive precisely because it's invisible until it isn't, until a report is wrong, a customer record is duplicated, or a compliance audit finds a gap nobody noticed. We build data quality controls directly into your pipelines instead of treating quality as something to check after the fact. That includes audit columns on every table so you can always trace where a record came from and when it changed, history tables that preserve what the data looked like at any point in time instead of silently overwriting it, and automated validation checks that catch malformed, duplicate, or out-of-range records before they reach a report or downstream system. We set up monitoring that flags anomalies, like a sudden drop in record volume or a spike in null values, so issues get caught within hours, not discovered three months later during an audit. For regulated industries like healthcare and finance, we also build in the documentation and lineage tracking compliance teams need to prove where data came from and how it was transformed. The goal is simple: by the time data reaches your dashboards, your finance team, or your compliance officer, you can trust it without double-checking it yourself.
Need help with data engineering services?
Send your requirements and we will respond with the best next step.
Contact GITS