You need a data engineer who can pick up your pipelines, not one who needs six weeks and a rewrite before they ship anything. Kiyansh Group places senior, US-based contract, contract-to-hire, and direct data engineering talent — Spark, Airflow, Snowflake, and the ETL glue in between. Every candidate is vetted by people who can read the code, and you talk to the engineer, not a hand-off desk offshore.
Tell us what you need →We staff data engineering roles three ways: contract for a defined build or backlog, contract-to-hire when you want to work with someone before committing to a headcount, and direct placement when you already know you need the role permanently. In every case the engineer is US-based and works with your team directly — no relay through an offshore delivery center, no midnight time-zone gap between you and the person writing the code. Where an H-1B contractor is the right fit for a role, we place them cleanly and handle the compliance side.
The engineers we place are senior. That means they can own a pipeline end to end: read the existing DAGs, understand the warehouse model, find where data is silently dropping, and fix it without breaking three downstream reports. They are comfortable joining a messy codebase mid-flight, which is the normal state of most data platforms, rather than only greenfield work.
We also build software directly with AI-augmented senior teams, so when a staffing conversation turns out to be a 'we actually need this built' conversation, we can take that on too. But for this page, the job is simple: get a qualified data engineer into your stack fast.
Resumes lie, and keyword-matched submittals waste your interview slots. Before a candidate reaches you, someone at Kiyansh who has actually shipped data pipelines screens them on real work — not trivia. We look at how they reason about a slow Spark job (partitioning, skew, shuffle, broadcast joins), how they structure an Airflow DAG for idempotency and backfills, and how they'd model a slowly changing dimension in Snowflake without melting your credits.
The screen is practical. We ask candidates to walk through a pipeline they built, then push on the decisions: why that file format, why that partition key, what happened when it failed at 2am, how they caught the bad data before the business did. Someone who only ran other people's DAGs cannot answer those questions in specifics, and it shows within minutes. We also sanity-check work authorization and availability up front, so a candidate you like doesn't evaporate at the offer stage.
The result is a shortlist you can act on, not a stack of maybes. You get a small number of people who fit the actual role, each with notes on where they're strong and where they're thinner, so you can interview for the gaps instead of rediscovering the basics.
Core to the role: Apache Spark (PySpark and Spark SQL, plus a real grasp of the execution model, not just the API), Apache Airflow for orchestration, and Snowflake as the warehouse — including cost-aware modeling, warehouse sizing, and query tuning. Around that, we screen for solid SQL, Python, and the ETL/ELT patterns that hold a platform together: incremental loads, CDC, schema evolution, and data quality checks that fail loudly instead of silently corrupting a table.
Adjacent tooling comes up constantly and we account for it: dbt for transformation, cloud object storage and the surrounding services on AWS, Azure, or GCP, Kafka or Kinesis for streaming, and table formats like Delta, Iceberg, or Parquet. If your stack leans on a specific piece — say Databricks, or Airflow on managed MWAA — we match for that rather than hoping a generalist figures it out.
On certifications: SnowPro, Databricks, and cloud data-engineering certs (AWS, Azure, GCP) are a useful signal that someone invested in the platform, and we note them. They are not a substitute for demonstrated work, and we don't let a cert paper over an engineer who can't explain a shuffle. We weight what someone has actually built above what they've tested for.
Pick the model that fits the risk. Contract works when you have a defined scope and a timeline. Contract-to-hire lets you evaluate fit on real work before converting to a permanent seat. Direct placement makes sense when the role is clearly long-term and you'd rather not carry a contract rate. We'll tell you honestly which one fits your situation instead of pushing whichever pays us more.
On timing: for a standard senior data engineering contract role, expect a qualified shortlist in roughly one to two weeks, faster when the requirements are tight and slower when the stack is unusual or the rate is below market. Highly specialized combinations — heavy streaming plus a niche warehouse plus a security clearance, for example — take longer, and we'll say so at the start rather than promise a week and miss it.
We also place as a sub-vendor to MSPs and prime vendors on both private-sector and government engagements, so if you're a prime who needs vetted data engineering talent under an existing contract, that's a normal engagement for us, not a special case.
US-based, and the engineer works with your team directly. There is no offshore delivery center between you and the person writing the code. Where an H-1B contractor is the right fit for a role, we place them cleanly and handle the compliance side — but the work stays with the engineer you interviewed.
Someone at Kiyansh who has shipped data pipelines screens each candidate on real problems — Spark performance, Airflow idempotency and backfills, Snowflake cost and modeling — and pushes on the specific decisions behind pipelines they've built. Engineers who only ran someone else's DAGs can't answer in specifics, and that surfaces fast. You get a short, honest shortlist instead of a stack of maybes.
For a standard senior data engineering contract, expect a qualified shortlist in roughly one to two weeks — faster with tight requirements and a market rate, slower for unusual stacks, niche combinations, or clearances. We give you a realistic timeline at the start rather than promise a week and miss it.
Tell Kiyansh Group what your data platform runs on and what you need built — we'll come back with vetted, US-based data engineers who can start, not a stack of resumes.
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