--- title: "Data Engineering" description: "Pipelines, warehouses, and streaming systems built for decision-grade output." url: https://www.expandware.com/solutions/data-engineering/ section: "Solutions" topics: ["ETL and ELT pipeline design and orchestration with Airflow, dbt, and Dagster.", "Data warehouse and lakehouse architecture on Snowflake, BigQuery, and Redshift.", "Real-time streaming pipelines with Kafka and Kinesis."] publisher: "Expandware Private Limited" --- # Data Engineering Pipelines, warehouses, and streaming systems built for decision-grade output. Data infrastructure your decisions can stand on. Dashboards are only as honest as the pipelines behind them. We build the layer underneath: ingestion, transformation, warehousing, and streaming, with the quality and lineage controls that make the numbers defensible in a board meeting. What decision-grade data requires. The dashboard is the last mile. Reliable decisions depend on governed inputs, observable transformations, and a definition of truth that survives scrutiny. Trusted data contracts. Sources, schemas, ownership, freshness expectations, and failure handling are explicit so downstream users are not left guessing what changed. Pipelines that can be operated. Orchestration, testing, alerting, lineage, and replay paths give your team an answer when data is late, incomplete, or wrong. Metrics leaders can defend. Business definitions are modelled with the data, linking reports to their source logic rather than to undocumented spreadsheet interpretation. What we deliver: - ETL and ELT pipeline design and orchestration with Airflow, dbt, and Dagster. - Data warehouse and lakehouse architecture on Snowflake, BigQuery, and Redshift. - Real-time streaming pipelines with Kafka and Kinesis. - Data quality, lineage, and governance frameworks. - Business intelligence dashboards and self-serve analytics enablement. How an engagement runs: Ingest, Sources, legacy systems, and spreadsheets. Transform, Modelled, tested, and versioned. Govern, Quality, lineage, and an owner per metric. The result: Numbers that survive scrutiny, One definition per metric, traceable back to source. Dashboards are only as honest as the pipelines behind them. How engagements run. It starts with a paid assessment: Anything carrying real technical risk begins with a fixed-fee technical assessment, one to three days, producing a written findings document and a scoped fixed price for the build. Fixed price for defined scope: Once the scope is known from your systems rather than from a conversation, the build is priced as a fixed figure, with the assumptions it depends on written down alongside it. Monthly retainer for operations: Ongoing operational ownership runs on a monthly retainer against an agreed service level, so the cost of running a system is a number you can plan against. No hourly meters running against unknowns. Common questions: Q: Can you consolidate data from legacy systems and spreadsheets? A: Yes. We assess source quality and ownership first, then design a staged ingestion and reconciliation plan so the target platform does not simply centralize unreliable data. Q: Do you build dashboards as well as pipelines? A: Yes, where the reporting decision is part of the mandate. The priority is always the governed data model and the quality controls beneath the dashboard. Q: Our numbers disagree between systems. Can that be fixed? A: Usually, and the fix is rarely technical first. Two systems disagree because they define the metric differently, so the work starts with agreeing the definition and an owner for it, then enforcing that definition in the pipeline. Q: Do we need a warehouse, or is our database enough? A: Frequently your database is enough, and we will say so. A warehouse earns its cost when reporting load threatens production, when you are joining across sources, or when you need history the operational system does not keep. Q: How do you handle personal data in a pipeline? A: Classification first, then minimization: fields that do not need to travel do not travel. Where data must move, we design for residency, retention, and the ability to delete a record everywhere it landed. Q: Can you work with the tools we already license? A: Yes. Replacing a working tool is expensive and disruptive, so our default is to make your existing stack do the job properly before proposing anything new. --- Canonical page: https://www.expandware.com/solutions/data-engineering/ Site index for machines: https://www.expandware.com/llms.txt Full site text: https://www.expandware.com/llms-full.txt Expandware Private Limited. Inquiries: solutions@expandware.com, +92 (333) 32 11011.