Data engineering services
Data engineering services: ETL and ELT pipelines, data warehouses and lakehouses on Snowflake, Databricks, BigQuery, Amazon Redshift and Microsoft Fabric, Spark processing, dbt modelling, streaming with Kafka, and data platforms for analytics and AI, with dedicated data engineers for your team or a data platform delivered end to end.
Data engineering we take on
Ingestion and ETL pipelines
Loads from databases, SaaS APIs, files and event trackers with Airbyte, Fivetran, dlt or custom Python, scheduled in Airflow 3, Dagster, Azure Data Factory or AWS Glue, incremental where possible and safe to re-run, plus reverse ETL back into your CRM and product.
Warehouses and lakehouses
Snowflake, Databricks, BigQuery, Amazon Redshift and Microsoft Fabric, or a lake on S3 or Azure Data Lake Storage with Apache Iceberg or Delta Lake tables, organised in raw, cleaned and business-ready layers.
Spark and large-scale processing
Batch jobs in PySpark and Spark SQL on Databricks, Amazon EMR or Fabric, partitioned and tuned so a nightly run finishes in its window, and DuckDB or Polars where the data fits on one machine.
Data modelling in dbt
Star schemas and Data Vault models as SQL in Git, with tests on keys, relationships and accepted values, documentation and lineage generated from the code, and every change run in CI before it merges.
Streaming and change data capture
Row-level changes captured from operational databases with Debezium, and events carried through Kafka, Amazon Kinesis or Azure Event Hubs and processed in Flink or Spark Structured Streaming, for data that has to be seconds or minutes old rather than a day.
Data quality and governance
Freshness, volume and schema checks with dbt tests, Soda or Great Expectations and alerts on failure, a catalogue with lineage in Unity Catalog, DataHub or OpenMetadata, access granted per role, and personal data masked outside production.
Moves off legacy platforms
SSIS packages, on-premises SQL Server and Oracle warehouses, Teradata and Hadoop clusters moved to a cloud platform in stages, with row counts and totals reconciled between old and new before anything is switched off.
Data for machine learning and AI
Feature pipelines and feature stores such as Feast or the one built into Databricks, point-in-time correct training sets, and the chunking and embedding pipelines that keep a vector index for retrieval in step with the source documents.
Platform cost and performance
Warehouse and cluster spend broken down per pipeline and per team, then cut with incremental models, partitioning and clustering, right-sized compute and auto-suspend, with query times measured before and after.
Hire data engineers
Dedicated data engineers
Data engineers who join your team full time, work in your tools and process, and report to your lead. You interview them; we carry the Ukrainian contract, payroll, invoicing and leave.
Data engineering projects
A defined piece of data engineering with a scope, a fixed plan and a named lead on our side who owns the result and reports progress in your channels.
Ongoing data engineering
Data engineering as a continuing service: the same people every month, a backlog you prioritise, and hours you can see in our portal and on the invoice.
The dedicated team page explains how specialists join your team, and the outsourcing page covers project delivery, take-overs and how we charge.
Who works on your data engineering
Data engineers
Pipelines, Spark, dbt and streaming
Analytics engineers
dbt models and metric definitions
Python engineers
Custom connectors and API ingestion
Cloud and DevOps engineers
Terraform and CI for the data platform
BI developers
The reports at the end of the pipeline
Choosing a data platform
Microsoft Fabric when the company already works in Microsoft 365 and Power BI. Databricks when Spark-scale processing or machine learning runs on the same data. Snowflake when the work is mostly SQL and the team wants little to operate. BigQuery or Redshift when the rest of the estate is on Google Cloud or AWS. PostgreSQL or Azure SQL with dbt on top when there are a handful of sources and modest volumes.
Whichever it is, the platform is defined in Terraform, pipelines and models live in Git and deploy through CI with their tests, development and production are separate, and secrets come from a vault rather than a notebook.
Other data and AI services we provide
AI and machine learning
AI and machine learning development services: forecasting, fraud detection, recommendation systems, computer vision, NLP and speech recognition, and MLOps, with dedicated machine learning engineers or project delivery.
Generative AI development
Generative AI development services: RAG over your documents, AI agents, chatbots, LLM features, document processing, fine-tuning, evaluation and voice agents, with dedicated AI developers or project delivery.
Data analytics and Power BI
Data analytics and Power BI services: dashboards in Power BI, Tableau and Looker, product analytics, A/B testing, financial and marketing reporting, with dedicated data analysts and BI developers or project delivery.
Database development
Database development and administration services: schema design, query tuning, migrations and backups on SQL Server, PostgreSQL, MySQL, Oracle and MongoDB, with dedicated database developers and DBAs or project delivery.
Computer vision development
Computer vision development services: object detection, visual inspection, OCR, video analytics, medical imaging and models on edge devices, with dedicated computer vision engineers or project delivery.
Data annotation
Data annotation and labelling services for AI: image, video, text, audio and LiDAR labelling, RLHF and evaluation data, with quality control on every batch, from dedicated data annotators or project delivery.
MLOps
MLOps services and consulting: ML platforms, CI/CD for models, model registries, serving and autoscaling, GPU infrastructure, drift monitoring and LLMOps, with dedicated MLOps engineers or project delivery.
Databricks development
Databricks development and consulting: lakehouse set-up, Lakeflow pipelines, Unity Catalog, migrations, AI/BI, machine learning and cost control, with dedicated Databricks engineers or project delivery.
Snowflake development
Snowflake development and consulting: warehouse design, Snowpipe and Openflow ingestion, dbt, migrations, Cortex AI, security and cost control, with dedicated Snowflake developers or project delivery.
Kafka and event streaming
Apache Kafka development and consulting: event-driven architecture, Kafka Connect and Debezium, Flink and Kafka Streams, Confluent Cloud, MSK and Event Hubs, with dedicated Kafka engineers or project delivery.
Workflow automation
Workflow and AI automation services: n8n, Make, Zapier and Power Automate flows, AI agents in workflows, CRM, finance and document automations, with dedicated automation developers or project delivery.
Questions about data engineering
Which industries do your data engineers work in?
AI and data products, where pipelines feed models and customer-facing analytics; fintech, for transaction, risk and regulatory reporting data; e-commerce and retail, for orders, inventory and clickstream; healthcare, for clinical and claims data under strict access rules; advertising and marketing technology, for high-volume event streams and attribution; and enterprise SaaS, for product usage data and reporting inside the product.
Which tools and platforms do you work with?
Snowflake, Databricks, BigQuery, Amazon Redshift, Microsoft Fabric and ClickHouse for storage and compute; Spark, dbt, DuckDB and Polars for processing; Airflow, Dagster, Prefect, Data Factory and AWS Glue for orchestration; Kafka, Kinesis, Event Hubs, Flink and Debezium for streaming; and Python and SQL throughout.
Can you take over pipelines another team built?
Yes. A take-over starts with a read-only review of the pipelines, the schedules and the failures that go unnoticed, and a written list of risks in order of impact, before anything changes.
How quickly can data engineers start?
When the right data engineer is available, the start is gated only by your interview and the NDA and IP assignment. Otherwise we run a search, which typically produces candidate profiles within two to three weeks, and nobody starts until you have said yes.
How do we hire data engineers through BigTree108?
Tell us the work, the seniority and the hours you need. We propose one or two people with their profiles, you interview them the way you would interview your own hire, and you sign one agreement with BIG TREE 108 LLC and receive one invoice a month.
Who owns the work they produce?
You do. Every specialist has a signed contract with BigTree108 that assigns all work product to the company, and our agreement with you assigns it onward. Code, designs and documents are delivered into your own repositories and tools, not kept where only we can change them.
Need your data in one place?
Tell us your sources, your volumes and what the data has to feed. You get an answer within one business day: a plan, a platform recommendation, or candidate profiles.