Data platform engineering
Pipelines your analytics team can trust. Data engineers who own quality.
When the board deck disagrees with ops, the failure often started weeks earlier in a silent Airflow run. We staff data engineers who debug backfills, tune Spark for cost, write dbt tests that catch bad joins early, and map lineage auditors can follow. Pipelines run with SLAs, owners, and quality gates instead of cron jobs nobody admits to owning.
Scope your data platform needsfrom airflow import DAGfrom airflow.providers.apache.spark.operators.spark_submit import SparkSubmitOperatorfrom airflow.providers.common.sql.operators.sql import SQLExecuteQueryOperatorfrom datetime import datetime, timedeltawith DAG( "daily_revenue_pipeline", schedule="@daily", start_date=datetime(2024, 1, 1), catchup=False,) as dag: transform = SparkSubmitOperator( task_id="normalize_orders", application="jobs/normalize_orders.py", ) dbt_run = SQLExecuteQueryOperator( task_id="dbt_run_marts", conn_id="snowflake", sql="dbt run --select marts.revenue", ) transform >> dbt_runCore stack
- Apache Airflow
- Apache Spark
- dbt
- Snowflake / BigQuery
- Kafka & schema registry
- Python pipelines
5+
Average years in production data engineering
Engineers who've owned pipeline on-call, not only notebook prototypes.
Deep-Dive Tech Stack
A data platform is only as trustworthy as orchestration, compute, modeling, and streaming agree. We match on the tools you run and engineers who treat pipelines like production services with on-call, not one-off scripts.
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Apache Airflow
DAGs with idempotent tasks, SLA sensors, and backfills that do not duplicate production data. Pool tuning keeps one heavy job from blocking nightly loads; after downtime they prevent catchup=True from avalanching the scheduler.
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Apache Spark
Partition tuning, broadcast joins, and cluster sizing on EMR or Databricks. Shuffle spill and skew get profiled so job runtime and cloud spend drop when defaults were over-shuffling data.
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dbt
Staging and mart models with schema tests and freshness checks in CI. Documentation from YAML explains grain to analysts; promotion to prod is gated so manual runs do not bypass tests.
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Snowflake / BigQuery
Warehouse sizing, clustering, and query patterns that scale without runaway credits. Query timeouts, resource monitors, and dev sandboxes separated from production keep spend predictable.
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Kafka & schema registry
Streaming ingestion with Avro or Protobuf evolution and dead-letter topics for replay. Breaking schema changes fail CI before they poison downstream dbt models finance relies on.
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Python pipelines
Custom operators, type-hinted validation, and notebooks that graduate to scheduled jobs with pinned dependencies when a one-off becomes weekly leadership reporting.
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Great Expectations
Expectation suites on critical columns and gates that block refresh when distributions drift. Bad data stops at the pipeline boundary instead of in the board deck.
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Delta Lake / Apache Iceberg
ACID transactions on object storage, time travel for debugging bad refreshes, and schema evolution without full table rewrites. Late-arriving facts and backfills stay auditable when finance asks which version of a metric was in last month's board deck.
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Fivetran & Airbyte
Managed and open-source EL connectors with schema drift handling, incremental sync, and monitoring on row counts and freshness. Source API changes surface as pipeline alerts, not as silent gaps in downstream dashboards.
Data platform metrics that matter
- Average years in production data engineering
- 5+
- Typical time to first trusted dataset
- 2–4 wks
- Typical failed-run reduction
- 50%+
- Lineage documented for new pipelines
- 100%
Engineers who've owned pipeline on-call, not only notebook prototypes.
For scoped pipelines with source access, staging environments, and clear acceptance criteria.
After retry policy fixes, data tests, and ownership clarity on past engagements.
Sources, transforms, and consumers mapped before production promotion, not after an audit scramble.
Data engineering staffing, answered plainly
How do you handle time-zone crossovers?
Pipeline failures don't wait for standups. We align overlap for incident response and planning sessions, with async runbooks and Slack updates for handoffs across US, EU, and India teams.
Do your engineers work in our warehouse and orchestration accounts?
Yes. We operate in your Snowflake, BigQuery, Airflow, and Git repos under your access policies. We don't require migration to our tooling.
What is your approach to data quality?
Tests ship with the pipeline: schema checks, row counts, freshness SLAs, and dbt or Great Expectations suites in CI. Bad data stops before it reaches executive dashboards.
Can you integrate with our analytics team?
We document models, grain, and caveats analysts need. We don't throw tables over the wall without ownership or refresh SLAs.
Who owns the pipeline code and IP?
You do. All DAGs, dbt projects, and infrastructure code live in your repositories under your terms.
