Data engineering services from senior data platform teams

Ryz data engineering services give you senior data engineers who build the pipelines, warehouses, lakehouses and data quality checks your analytics and AI depend on, on Snowflake, Databricks, BigQuery, Redshift or Microsoft Fabric. They come from the top 1% of the engineers we interview, work on US business hours, and build in your cloud and repos, with transformations in dbt or Spark and orchestration in Airflow or Dagster. You get a dedicated team that owns a data platform build, or senior engineers who join your data team's sprints.

What we build

• Ingestion pipelines: batch and incremental loads from SaaS tools, application databases and files, using Fivetran or Airbyte where connectors exist and custom Python where they do not.
• Change data capture: Debezium, AWS DMS or native CDC streaming database changes into the warehouse, so reports are minutes old instead of a day old.
• Warehouses and lakehouses: Snowflake, Databricks with Delta Lake, BigQuery, Redshift or Fabric, with Apache Iceberg tables where you want an open format across engines.
• Transformation layers: dbt projects with staging, intermediate and mart models, tests, documentation and clear ownership per domain.
• Streaming pipelines: Kafka, Amazon Kinesis or Azure Event Hubs with Flink or Spark Structured Streaming for fraud signals, operational dashboards and event-driven features.
• Orchestration: Airflow, Dagster or Prefect DAGs with retries, backfills, SLAs and alerting, replacing cron jobs and scripts on someone's laptop.
• Data quality and observability: dbt tests, Great Expectations or Soda checks, and freshness and volume monitors, so broken data is caught before it reaches a dashboard.
• Data for machine learning and AI: feature tables, training datasets with point-in-time correctness, and document pipelines that feed retrieval systems for machine learning development and AI work.

How an engagement works

Talk. We map your sources, current warehouse, who consumes the data (BI, finance, product, ML), freshness needs, data volumes and governance requirements such as PII handling.

Match. We propose engineers with experience on your platform. Snowflake and dbt, Databricks and Spark, and streaming on Kafka with Flink are distinct skill sets, and we match to yours.

Join. Engineers work in your cloud, warehouse and repos, join standups and demo new models and pipelines to the people who will use them.

Grow. Add analytics engineers, BI developers or ML engineers as the platform matures, or hand over a documented platform to your team.

In week 1, the team typically inventories sources, pipelines and the reports people rely on, and identifies the data that most often breaks or disagrees. By month 1, the first sources run through the new pipeline into tested dbt models, with orchestration and alerting in place. By month 3, the usual picture is core business entities modeled once and reused, freshness and quality monitored, and legacy jobs being retired.

The stack our teams work in

How we keep data correct and pipelines reliable

Data platforms rarely fail loudly. They fail with a dashboard that is quietly wrong, a pipeline that silently stopped two days ago, or two teams reporting different revenue numbers. A senior data engineer designs against those failure modes:

• Tests on every model. Uniqueness, not-null, accepted values and referential checks in dbt, plus business rules such as "order totals match line items", run on every build.
• Freshness and volume monitoring. Alerts when a source stops arriving or row counts swing outside normal ranges, so a broken upstream API is noticed in hours, not at month-end.
• Idempotent, replayable pipelines. Loads that can rerun without duplicating rows, using merge keys and partition overwrites, so backfills are routine instead of risky.
• Schema change handling. Data contracts or schema checks with source teams, and pipelines that fail clearly on breaking changes rather than loading nulls.
• One definition per metric. Core entities and metrics modeled once, in a semantic layer such as dbt metrics or the BI tool's model, so finance and product read the same number.
• Late and out-of-order data. Watermarks and reprocessing windows in streaming jobs, and incremental models that pick up late-arriving records.
• Cost control. Warehouse sizing, auto-suspend, clustering and partitioning reviewed against query patterns, because unmanaged warehouse spend grows fast.
• Access and PII. Role-based access, column masking and row-level security for sensitive fields. Engineers have experience working within GDPR, CCPA, HIPAA and SOC 2 requirements.

Reliable data is also what makes AI work in production. One of our AI pod teams built fraud detection for a global fleet company that has surfaced $5.94M in confirmed fraud, validated by the client's fraud team. Read more in our case studies.

Team shapes and cost

Typical Ryz cost is $7,000 to $15,000 per engineer per month. Mid-level engineers are $7,000 to $10,000, senior engineers are $10,000 to $15,000, and leads are $15,000 or more, quoted per team.

• Data pair: 2 senior data engineers × $10,000 to $15,000 = $20,000 to $30,000 per month. Fits a new set of pipelines or a dbt rebuild.
• Platform build team: a data lead ($15,000+) plus 3 senior data engineers ($30,000 to $45,000) = from $45,000 per month. Fits a new warehouse or lakehouse with migration from legacy jobs.
• Data and analytics team: a lead plus 2 senior data engineers and 2 mid-level analytics engineers: $15,000+ plus $20,000 to $30,000 plus $14,000 to $20,000 = from $49,000 per month.

Every quote is scoped per team. You get a plan, a price and the names of the people before you start. See data engineer rates by seniority.

Dedicated team or staff augmentation?

A dedicated development team fits a defined platform build, such as a new lakehouse, a warehouse migration or a streaming system, owned from design to production. Staff augmentation fits when you have a data lead and need senior engineers who join your sprints and report to your leads. See our hire data engineers, Databricks engineers and Snowflake developers pages.

When Ryz isn't the right fit

If you want a packaged data platform product rather than engineers, talk to a platform vendor. If you need a data strategy engagement for the board without a build, a management consultancy fits better. If your team works on European or Asian hours, a global network will give you better overlap.

Related

• Software development services
• Business intelligence services
• AWS development services
• Azure development services

FAQ

Snowflake or Databricks?

Snowflake fits SQL-first analytics teams that want low operating effort. Databricks fits heavy Spark workloads, data science and machine learning on the same platform. Many companies run both, sharing Iceberg or Delta tables. We recommend based on your workloads and team.

How much do data engineering services cost?

Typical cost is $7,000 to $15,000 per engineer per month. Two senior data engineers run $20,000 to $30,000 per month, and a lead plus three seniors starts at $45,000 per month. Warehouse and cloud usage are billed separately by your providers.

How fast can a data engineering team start?

After the scoping call we propose a team with names. Most of the timeline depends on scope and on your onboarding, especially access to sources, the warehouse and cloud accounts.

Do we need streaming or is batch enough?

Most analytics is fine with hourly or daily batch. Streaming is worth its extra complexity for fraud signals, operational monitoring and features that react to events in seconds.

Can you migrate us off legacy ETL tools?

Yes. Moving from SSIS, Informatica or stored-procedure pipelines to dbt and a modern orchestrator is common work, done source by source with results reconciled against the old system.

Questions we didn't answer? Email info@ryzlabs.com.