Hire Data Engineers — Pipelines, Lakes & Warehouses

A data engineer builds and maintains the systems that collect, move, transform, and store data so that analysts, data scientists, and business users can work with reliable information. This includes ETL pipelines, data warehouses, streaming ingestion, orchestration systems, and data quality frameworks. Unlike a data analyst who queries data, or a data scientist who models it, a data engineer ensures the underlying infrastructure is correct, scalable, and trustworthy.

Standard placement is within 48 hours of contract signing for foundational data engineering work. For specialised streaming (Flink, Spark Streaming) or specific cloud data platforms (Databricks, Redshift), allow up to one week. We maintain available data engineering capacity at all times.

Our primary platforms are Snowflake and BigQuery. We also work with Amazon Redshift, Databricks, and ClickHouse for analytical workloads. The warehouse choice depends on your existing cloud provider, query patterns, and team familiarity. We help you evaluate options during scoping and migrate between platforms when the need arises.

dbt (data build tool) is our standard transformation layer for data warehouses. Our engineers write SQL models with clear dependency graphs, add schema tests for data quality assertions, generate documentation from model descriptions and column-level metadata, and run models in CI on every pull request. dbt makes data transformation code reviewable, testable, and auditable — the same engineering discipline applied to analytics code.

Reliable pipelines require idempotent transformations (safe to re-run), atomic writes (all or nothing), clear error handling with alerting, and schema validation at ingestion. We implement checkpointing for long-running pipelines, dead-letter queues for failed records, and reconciliation checks that verify source record counts match destination counts after each load. Reliability is designed in, not bolted on.

Yes. We build Kafka-based streaming pipelines using Kafka Connect for source connectors, Kafka Streams or Apache Flink for stream processing, and Kafka Connect sink connectors to deliver processed data to warehouses or operational stores. For simpler real-time requirements, AWS Kinesis or Google Pub/Sub with Dataflow are lighter-weight alternatives we also work with.

Schema changes in source systems are a constant reality. We implement schema registries (Confluent Schema Registry for Kafka) to enforce compatibility, use schema-on-read formats (Parquet, ORC, Delta Lake) in data lakes that tolerate additive changes, and write migration scripts for warehouse column additions. Breaking schema changes are flagged and communicated before they reach downstream consumers.

Yes. A lakehouse combines the low-cost storage of a data lake with the query performance and ACID transactions of a data warehouse. We implement this with Delta Lake on S3/GCS, exposing tables via Apache Spark or Trino for interactive queries and via dbt for transformation workflows. The lakehouse pattern is particularly effective when you have high ingestion volumes and need both batch and streaming access patterns.

Data quality is enforced at multiple layers: schema validation at ingestion, dbt tests (not_null, unique, relationships, custom SQL assertions) at transformation, and Great Expectations checks before data is published to production datasets. Failures trigger alerts and block pipeline completion — preventing bad data from silently flowing into downstream dashboards and models.

Yes. We ingest data from CRMs (Salesforce, HubSpot), marketing platforms (Google Ads, Meta), product analytics (Amplitude, Mixpanel), payment processors (Stripe), and any source with an API. We use Fivetran or Airbyte for commodity connectors and write custom ingestion scripts for sources without pre-built connectors. All ingested data is validated and catalogued before entering the warehouse.

Yes. Documentation is a deliverable, not an afterthought. We use dbt's documentation site to publish lineage graphs and column-level descriptions, maintain a data dictionary for business-critical metrics, and write runbook documentation for pipeline operations. Analysts should be able to understand where a number comes from without asking the data engineer who built the pipeline.

We offer monthly retainer and time-and-materials models. For greenfield data platform builds, a fixed-scope project with a defined deliverable list is also available. All models include weekly progress reports, transparent timesheets, and a dedicated account manager. Minimum engagement is one month — most data platform projects run three to six months for initial build, then transition to ongoing retainer for maintenance and feature work.