Hire Data Engineers
With Softeko
ready to start in 72 hours.
60+
Data Engineers
25+
Production Pipelines
95%
Client Repeat Rate
90+
Play Store Releases
Vetted Data Talent
Get the right talent fast, start building in just 2-3 days.
Fast Onboarding
Only the best pass rigorous vetting process.
Innovative Projects
Hire one expert or a full team, scale as needed.
Proven Results
With the project - every step to ensure success.
Skip the Hassle of Recruitment
Onboard our senior data Engineers in a matter of days. This is just a small sample of the high-caliber talent working for us already.
✓
Modeled marts with dbt and contracts; automated ELT via Fivetran; enforced tests, freshness,
and docs for trusted analytics in Snowflake.
Chattogram, Bangladesh 4–6h overlap (EU)
✓
Embedded data quality with Great Expectations; GitOps + Terraform for repeatable stacks;
alerting on freshness, volume, and schema drift.
Khulna, Bangladesh • 4–6h overlap (CET)
✓
Built lakehouse on Delta Lake with ACID streams; orchestrated batch + CDC in Airflow,
and tuned Spark for cost and throughput.
Dhaka, Bangladesh • 4–6h overlap (CET))
Delivered exactly-once streams using Kafka + Schema Registry and Flink;
backpressure controls and DLQs kept SLAs during spikes.
Sylhet, Bangladesh • 3–5h overlap (UK)
✓
Re-platformed to BigQuery with partitioning, clustering, and materialized views; standardized SQL with Dataform and strong code review.
Rajshahi, Bangladesh • 4–6h overlap (EST)
Implemented 3-D Secure payments and offline caching for a delivery app; targeted FCM campaigns increased
reorders by 24%. Deep experience with Retrofit/OkHttp interceptors, resilient
Room sync, and Firebase Analytics for growth experiments.
São Paulo, Brazil • 2–4h overlap (ET)
Top Data Engineers,
Ready When You Are
Skip weeks of screening. Get instant access to pre-vetted android experts who can:
- Build scalable, high-performance systems
- Contribute from day one, no hand-holding required
- Align with your stack, tools, and workflows
- Collaborate seamlessly with existing teams
- Hit sprint goals without onboarding delays
Services Our Data Engineers Offer
From startups to enterprises, our Data Engineers deliver platforms that perform on every device and every release.
Data Ingestion & Integration
Batch, CDC, ELT with Fivetran/Glue.
ETL/ELT & Orchestration
Airflow/Prefect, retries, SLAs, lineage.
Streaming & Real-time Pipelines
Kafka/Flink/Kinesis with exactly-once.
Data Modeling & Warehousing
dbt/SQL for Snowflake/BigQuery/Redshift.
Lakehouse & Storage
Delta/Iceberg/Hudi on S3/GCS/Azure.
Data Quality & Governance
Great Expectations, contracts, and catalogs.
Performance & Cost Optimization
Partitioning, clustering, caching, pruning.
MLOps & Feature Stores
Batch/online features, versioned datasets.
DataOps & CI/CD
GitOps, tests, and environment promotion.
Our Operational Blueprint: How Softeko Works
Our proven methodology ensures successful project delivery from concept to deployment.
-
Step 1
Discover Needs
We start by understanding your workflows, pain points, and goals.
→ Analysis -
Step 2
Build Strategy
We design a roadmap customized to your tech, team, and timelines.
→ Planning -
Step 3
Assign Experts
Your project is powered by a dedicated, domain-aligned team.
→ Matching -
Step 4
Deliver in Sprints
We execute in agile sprints with full transparency and feedback.
→ Execution -
Step 5
Optimize Continuously
Post-launch, we refine and adapt to ensure lasting results.
→ Enhancement
Why Hire Data Engineers With Softeko?
Spark & Compute
Fast, scalable processing.
Airflow & Orchestration
Reliable, observable pipelines.
dbt & SQL Models
Tested, documented transforms.
Kafka & Streaming
Low-latency, exactly-once ETL.
Warehousing Platforms
Snowflake, BigQuery, Redshift.
Quality & Lineage
Expectations, contracts, lineage.
Flexible Engagement Models
Scale your team up or down to exactly the size you need:
- Dedicated Pods : 1–3 developers fully focused on your roadmap
- Staff Augmentation : integrate seamlessly with your in-house squad
- Short-term Sprints : bring on experts for rapid feature bursts
- Long-term Partnerships : retain knowledge, avoid ramp-up delays
100% Vetted Talent
Only the top 1% of Data Engineers pass our rigorous screening.
72-Hour Onboarding
Your first expert codes within three days, no delays.
Effortless teamwork
Engineers adapt instantly to your tools, processes, and culture.
Guaranteed Results
We tie delivery milestones directly to your KPIs.
7-Day Pilot Engagement
Risk-free trial, onboard a data engineer for one sprint and see immediate impact.
How Long Does It Take to Hire Data Engineers?
| Platform | Avg. Time to Hire | What’s Involved |
|---|---|---|
| Traditional Job Boards | 10–14 days | Job posts, resume screening, multi-round interviews, onboarding paperwork |
| In-House Recruiting | 3–6 weeks | HR screening, technical tests, salary negotiation, notice periods |
| Softeko Data Talent Pool | 24–48 hours | Pre-vetted Data Engineers ready to start immediately |
Launch Your Project in 2 Business Days
No job-board delays. Zero sourcing overhead. Hire Data Engineers instantly and hit the ground running.
Interview Questions to Ask Before You Hire Data Engineers
Identify the right fit faster with these targeted technical and behavioral questions.
Data Modeling & Warehousing
Star vs snowflake schema, difference?
Star uses denormalized facts + dimensions; snowflake normalizes dimensions to reduce duplication.
Surrogate vs natural keys?
Surrogate (e.g., uuid) is stable/opaque; natural keys carry business meaning but can change.
SCD Types I/II, when to use?
Type I overwrites values; Type II adds a new row with validity ranges for history.
Partitioning vs clustering?
Partition prunes files by key; clustering/sorting improves scans within partitions.
ETL/ELT & Orchestration
ETL vs ELT, pick when?
ETL transforms before load (legacy/limited warehouses); ELT loads first and transforms inside MPP engines.
Airflow DAG best practices?
Idempotent tasks, small units, retries with backoff, clear SLAs, and data-aware scheduling.
Backfill strategy?
Run range jobs with fixed inputs, immutable outputs, and checkpointed state; avoid double writes.
Idempotency, how enforced?
Dedup on idempotency_key, upserts/merges, and exactly-once sinks.
Batch Processing (Spark)
Wide vs narrow transformations?
Narrow stays on one partition; wide shuffles data (e.g., groupBy), more costly.
Skew handling techniques?
Salting keys, adaptive query execution (AQE), broadcast joins, and better partitioning.
Why cache/persist?
Reuse expensive results; choose storage level (MEMORY_ONLY/MEMORY_AND_DISK) based on size.
Optimize Parquet reads?
Predicate pushdown, column pruning, proper stats, and avoiding tiny files.
Streaming & Real-time (Kafka/Flink/Spark)
Event time vs processing time?
Event time is when the event happened; processing time is when it’s handled.
Watermarks, purpose?
Bound lateness for windows; evict state after watermark delay.
Exactly-once, how?
Use transactional sinks, idempotent producers, and consistent checkpoints.
Out-of-order events?
Use windowing with allowed lateness + dedup by key + sequence/offset.
Data Quality & Testing
Great Expectations/dbt tests, role?
Assert schema, ranges, freshness; break builds on violations.
Data contracts, definition?
Versioned schemas + SLAs between producers/consumers; changes reviewed and backward-compatible.
Detect schema drift?
Schema registry, inferredSchema diffs, and alert on unexpected fields/types.
Nulls and defaults strategy?
Prefer explicit defaults and COALESCE; document nullable columns in the contract.
Red Flags to Watch For
⭕ No lineage, no data tests.
⭕ Only batch; ignores streaming.
⭕ Manual pipelines; no orchestration.
⭕ No CI/CD pipeline familiarity
Additional Interview Questions
Storage & Lakehouse (Delta/Iceberg/Hudi)
Why Parquet/ORC columnar?
Columnar compression + predicate pushdown reduce I/O and cost.
Small files problem, fix?
Compact files, tune targetFileSize, and batch writes.
Delta vs Iceberg vs Hudi?
All add ACID + metadata; differ in merge/compaction features and catalog integration.
Time travel, use case?
Reproducible reads, audits, rollback; e.g., VERSION AS OF.
Performance & Cost Optimization
Z-ORDER / clustering, why?
Co-locates correlated columns to speed selective queries.
Choose partition keys, how?
High cardinality hurts; pick date/org/region—balanced size and pruning.
Warehouse credits control?
Use query queues, budgets, auto-suspend/resize, and materialized views.
Caching tiers?
CDN/BI cache, warehouse result cache, and data cache (e.g., Spark cache()).
Security, Governance & Privacy
PII handling in lakes?
Tag columns, tokenize/encrypt, and restrict via row/column-level security.
RBAC vs ABAC?
RBAC uses roles; ABAC evaluates attributes (user/resource/context) for fine-grained control.
GDPR “right to erasure”?
Locate subject data, delete/purge across tables, re-compact files, update indexes.
Secrets management?
KMS/Vault, short-lived creds, no secrets in code or logs.
Operations, Reliability & CI/CD for Data
SLI/SLO for pipelines?
Freshness, completeness, and success rate with targeted SLOs.
Monitoring, what to track?
Lag, throughput, error rates, queue depth, and checkpoint ages.
CI/CD for data code?
Test SQL/dbt, validate schemas, run sample jobs, deploy via GitOps.
Disaster recovery basics?
RPO/RTO targets, cross-region replicas, tested restores, and runbooks.
Checkout Other Experts
With our IT staff augmentation services, you skip the headaches of hiring and managing admin tasks. We handle all the legwork, so you get top-notch specialists with real-world experience, ready to dive into your project with no hassle and no wasted time.
Testimonial
Since 2013, Softeko has helped businesses scale efficiently with top-tier IT professionals. Our customized IT staff augmentation services bridge talent gaps and boost your team’s productivity with speed and flexibility.
Questions? We've Got Answers.
1. What technologies do your Data Engineers specialize in?
Spark/Scala/PySpark, Airflow/Prefect, Kafka/Flink, dbt/SQL, Delta/Iceberg/Hudi, Snowflake/BigQuery/Redshift, and AWS/GCP/Azure.
2. Can I hire for short-term delivery?
Yes. Whether you need to build fast or scale support, we offer flexible engagement models.
3. How fast can I onboard someone?
We can match you with vetted android developer and initiate onboarding within 48-72 hours.
4. Will I get to interview the developers?
Absolutely. You’ll have the option to interview and assess shortlisted developers before making a final decision.
5. Are the developers available in my time zone?
Yes. We provide global talent with overlapping work hours and full-time availability in your preferred time zone.
6. Can I scale the team up or down?
Yes. Scale up during critical phases or reduce size post-release—no long-term lock-ins.
With Softeko