Services

Data Engineering

Modern data stacks from ingestion to serving. Governance, quality checks, and cost visibility built in.

We build modern data pipelines that move data from source systems to analytics and applications reliably and efficiently. Our approach emphasizes data quality, governance, and cost visibility from day one.

Typical Deliverables

Ingestion pipelines

Automated data ingestion from APIs, databases, files, and streaming sources with schema validation and error handling.

Quality checks

Data quality frameworks with checks for completeness, freshness, accuracy, and consistency. Alerts on violations.

Cost guardrails

Monitoring and budget alerts to prevent cost overruns. Recommendations for optimization.

Data catalogs

Documentation and lineage tracking so teams can discover and trust data assets.

Tooling

We're tool-agnostic and choose based on your needs. Common tools include:

  • • Airflow, Prefect, or Dagster for orchestration
  • • dbt for transformations
  • • Great Expectations or custom validators for quality
  • • Snowflake, BigQuery, Redshift, or Databricks for warehousing
  • • Kafka, Pulsar, or managed streaming for real-time
# Example: Data quality check
from great_expectations import expect

def validate_user_table(df):
    return (
        expect(df).to_have_rows_at_least(min_value=1),
        expect(df).columns_to_match_set(column_set={"user_id", "email"}),
        expect(df["email"]).to_be_valid_email(),
    )

Outcomes

Data freshness

Sub-hour latency for critical pipelines

Quality coverage

90%+ of critical fields validated

Cost efficiency

20-30% reduction through optimization