Hire Data Engineersin 2 weeks
BearPlex data engineers build the pipelines, warehouses, and infrastructure that turn fragmented operational data into the analytics-ready and AI-ready foundation modern companies depend on. Snowflake, BigQuery, Databricks, Kafka, dbt: production engineering, not no-code stitching.
What a Data Engineer actually does at BearPlex
A data engineer at BearPlex owns the full data pipeline lifecycle: source ingestion (Fivetran, Airbyte, custom CDC, Kafka), warehouse modeling (dbt, SQL, dimensional design), transformation pipelines (batch and streaming), data quality engineering (tests, observability, lineage), and operational ownership of the resulting systems. They work across the modern data stack: Snowflake, BigQuery, Databricks, ClickHouse for warehousing; Kafka, Kinesis, RabbitMQ for streaming; Airflow, Dagster, Prefect for orchestration; dbt for transformation; Hightouch and Census for reverse ETL, and know which tools fit which problems. They've shipped pipelines that handle 50B+ events per month, built customer 360 models that reconcile identity across 8+ source systems, and stood up AI-ready feature stores for production ML. Importantly, they push back on the 'Modern Data Stack' diagram when it doesn't fit the actual problem, sometimes the right answer is Postgres + cron, not Snowflake + Airflow + dbt + Hightouch.
Sample engineer profiles
Anonymized to respect engineer privacy. Full bios shared under NDA during scoping.
Built the data platform for a Series C B2B SaaS: 600+ dbt models, 8 source systems unified, customer 360 powering sales, marketing, and product analytics.
Designed the streaming pipeline for a logistics scale-up: handles 4B events/day with sub-5-second end-to-end latency, replaces batch system that ran 4 hours behind.
Migrated a Series B fintech from a brittle Mongo + cron + Redash stack to BigQuery + dbt + Hightouch in 10 weeks: analytics that took days now ship in hours.
Led data lakehouse architecture for a US healthcare client: handles 50TB+ of clinical and claims data with HIPAA-compliant access controls and audit logging.
Skills matrix
The capabilities every BearPlex Data Engineer brings on day one.
| Skill | Proficiency | Typical tools |
|---|---|---|
| Modern data warehouse design (Snowflake, BigQuery, Databricks) | Expert | Snowflake · BigQuery · Databricks SQL Warehouse |
| dbt modeling and best practices | Expert | dbt Core · dbt Cloud · dbt-utils · dbt-expectations |
| ETL/ELT pipeline development | Expert | Fivetran · Airbyte · Stitch · custom Python connectors |
| Stream processing and event pipelines | Advanced | Kafka · Kinesis · Flink · Materialize · RisingWave |
| Workflow orchestration | Expert | Airflow · Dagster · Prefect · Argo |
| Data quality and observability | Expert | dbt tests · Great Expectations · Monte Carlo · Soda |
| Reverse ETL and operational analytics | Advanced | Hightouch · Census · Rivery |
| Data lakehouse architecture | Advanced | Delta Lake · Iceberg · Hudi · Spark · Trino |
| Identity stitching and customer 360 | Expert | dbt · custom matching algorithms · Snowflake Snowpark |
| Performance tuning warehouse queries | Expert | Snowflake query profiler · BigQuery execution plans · dbt incremental strategies |
| Data governance and access control | Advanced | Snowflake RBAC · BigQuery IAM · Atlan, Alation, Collibra |
| Cost optimization for cloud data platforms | Advanced | Snowflake resource monitors · BigQuery slot management · dbt model performance audits |
How we vet data engineers
Technical screen
60-minute deep-dive on a past data pipeline project. Candidate walks through architecture choices, schema design, transformation logic, and what they'd do differently. We screen out engineers who've only done 'turn the Fivetran knob' work: we want engineers who've designed pipelines from first principles.
Live SQL + dbt exercise
We give the candidate a realistic SQL/dbt problem (poorly-modeled source data, fix the dimensional model, write tests, document) and 90 minutes. We're looking for: clean SQL, sensible incremental strategies, meaningful tests, and pragmatic trade-offs.
Architecture interview
Whiteboard a data platform for a realistic client scenario: Series B B2B SaaS, 8 source systems, 50M events/month, executive analytics + product analytics + AI features. We probe for tool selection rationale, build-vs-buy thinking, ops awareness, and cost consciousness.
Reference checks + paid trial
Two engineering reference checks plus a 21-day paid trial on a real client engagement. We don't take engineers off trial until both Hamad and the client engineer report 'I want this person on the team next sprint.'
What clients say
“Their data engineer rebuilt our pipeline in 8 weeks that 3 different agencies had failed to deliver in 18 months. The difference: he asked what questions our analysts actually needed answered, not what tools we 'should' use.”
“We had a 'Modern Data Stack' that nobody could maintain. The BearPlex engineer simplified it down to what we actually needed and saved us $14K/month in tooling we weren't using.”
“Best dbt engineer I've worked with. The model layer he designed (sources, intermediate, marts) became the template for how our team builds everything now.”
Hiring data engineers: questions answered
Yes: dbt is core to most engagements. We follow dbt best practices: layered models (sources → intermediate → marts), tests on critical fields, exposures linking models to downstream use cases, incremental materialization where it matters, and well-organized model structure that the client team can extend.
Yes. Streaming pipelines for: real-time product analytics, usage-based metering, fraud detection, in-product personalization, event-driven AI workflows. We honestly assess whether streaming is required vs whether hourly batch would meet the actual business need: many 'real-time' requirements turn out to mean 'within 15 minutes,' which is much simpler.
Yes: common engagement pattern. Build the warehouse and clean event data, then layer a feature store (Tecton, Feast, or custom) for batch training and online inference. We pair data engineers with our ML engineers on these projects to ensure feature definitions align with actual model needs.
Yes: common in our healthcare, financial-services, and enterprise SaaS engagements. We implement data classification (PII, PHI, sensitive financial), row-level and column-level access controls, audit logging, retention policies, and right-to-deletion workflows. For SOC 2, HIPAA, and GDPR compliance, we design the data platform with compliance requirements as first-class constraints from day one.
Per source. Use Fivetran or Airbyte for SaaS-to-warehouse ingestion of standard sources (Salesforce, HubSpot, Stripe, Zendesk): the cost is real ($1-5K/month at growth-stage volume) but the engineering time saved is much higher. Build custom for high-volume product event streams, sources without managed connector support, or latency-critical paths.
Primarily Lahore, Pakistan (HQ) with client-facing presence in Austin and Doha. Time zone overlap with US clients is 5-9 hours; we structure engagements with daily 2-3 hour overlap windows for synchronous work, async handoff for the rest.
Yes: most engagements are co-developed with the client's existing data engineer or analytics engineer. We work in your GitHub, code-review with your team, and structure handover so your team owns the platform after we leave. The goal is augmenting your capacity to ship, not creating long-term dependency.
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