Staff augmentation · India
Hire Data Engineers in India.Vetted shortlists for your team.
Request pre-vetted data engineers in India. Shortlists in 2–3 weeks with clear commercial terms for pipeline and platform work.
Request profilesfrom 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 with production pipeline ownership experience.
How we staff engagements
We support founders and engineering leaders who need capacity without a long permanent hire cycle. You get clear timelines, a defined vetting process, flexible engagement options, and pricing before interviews.
- Placement speed
- 2–3 weeks
- Vetting process
- 4-step screen
- Engagement models
- Flexible
- Pricing range
- Custom bands
Role intake, sourcing, and technical screening through to a shortlist you can interview. Typical start within 2–4 weeks after you select a profile.
Stack and experience match, communication check, live technical review with a senior engineer, and reference checks before profiles reach you.
Dedicated augmentation, contract staffing, or contract-to-hire. Engineers work in your repositories, tools, and time zones, with IP assigned to you.
Monthly or hourly bands by seniority and stack, quoted before interviews so finance and engineering can align on budget.
Engagement metrics
- 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 with production pipeline ownership experience.
Scoped delivery once warehouse and source access are available.
Seen after retry, testing, and ownership improvements on past engagements.
New pipelines ship with source-to-consumer notes your team can maintain.
Skills screened
Tools we staff
Profiles are screened against your pipeline and warehouse tools.
Apache Airflow
DAGs, retries, backfills, and job ownership.
Apache Spark
Batch transforms and job cost tuning.
dbt
Models, tests, docs, and CI checks.
Snowflake / BigQuery
Warehouse modeling and query performance.
Kafka & schema registry
Streaming ingestion and schema changes.
Python pipelines
Python jobs that fit your data platform.
Great Expectations
Data tests on critical pipeline runs.
Delta Lake / Apache Iceberg
Lakehouse tables, partitions, retention.
Fivetran & Airbyte
Ingestion connectors with clear owners.
Frequently asked questions
How long does it take to receive a shortlist?
Most roles are shortlisted in 2–3 weeks after intake. Timing depends on seniority, stack rarity, and how quickly feedback comes back on the first profiles.
How do you vet candidates before we interview?
We screen for stack and experience fit, run a communication check, complete a live technical review with a senior engineer, and verify references before profiles reach you.
What engagement models do you support?
Dedicated staff augmentation, contract staffing, and contract-to-hire. Engineers join your tools, repositories, and ceremonies. IP is assigned to your company.
What if the person is not a fit after starting?
We replace in up to 30 days at no extra sourcing fee. Tell us early so we can protect your timeline.
How does pricing work?
We quote monthly or hourly bands by seniority and stack before interviews. That keeps finance and engineering aligned on budget before you invest time in the process.
