At blubank, we are seeking talented, dynamic, and enthusiastic individuals for the position of Data Engineer Team Lead to join our friendly and professional team. If you’re looking for a workplace where you can grow and continuously learn, this opportunity is for you!

Lead bluBank's Data Engineering team and own the ingestion pipelines and the lakehouse that analytics, risk, marketing, product, and regulatory reporting run on. Accountable for what the team ships and the engineers themselves.

Key Responsibilities:

• Plan sprints and prioritize the team backlog against the roadmap.
• Turn requests from product into engineering work with appropriate solutions.
• Manage dependencies and communications with development, DevOps, security, DBA, and third-party providers.
• Own the design of ingestion pipelines and lakehouse layers.
• Review architecture, designs, and code; set standards, keeping data models, contracts, and layering coherent.
• Stay hands-on for critical functions: critical CDC paths, backfill and reprocessing, performance, and cost problems.
• Assign work, remove blockers daily, and keep the team's progress visible.
• Run monthly and quarterly 1:1s, set objectives, and give direct feedback to engineers.
• Interview and onboard new engineers.
• Own the SLAs of the team's pipelines (freshness, availability, correctness), the on-call rotation, and incident response; require postmortems and fix root causes.
• Own reconciliation between source systems and the lakehouse. In a bank, quietly wrong data is worse than late data.

Technical requirements

• CDC extraction from transactional systems.
• Handle semantics like at-least-once vs exactly-once, idempotent writes, deduplication, ordering guarantees, out-of-order, late data, and backfill.
• Schema evolution, data contracts, and data quality checks.
• Handling at scale: partitioning and parallelism, consumer lag and backpressure, skew.
• Layering from raw to curated to serve medallion architecture.
• Open table formats: ACID and snapshot semantics, merge/upsert patterns, partitioning and file sizing, snapshot retention, and time travel.
• Modeling for consumption: dimensional models, slowly changing dimensions, incremental materialization, late-arriving facts, and when denormalization earns its cost.
• Data quality as a system: in-pipeline checks, freshness/volume/distribution monitoring, lineage, and reconciliation against sources.
• Strong SQL, plus production-grade code in Python, Java, or Scala.
• Containerized on-prem deployment; monitoring, alerting, and SRE-style operation of data services.
• On tools: our stack must be Kafka, Spark/Flink, Airflow, and PostgreSQL / Elasticsearch / MongoDB / Oracle sources.

Qualifications

• Breaks product requests into sized work, estimated, well-defined.
• Uses design and code review to teach and raise the standard.
• Gives direct, specific feedback.
• Explains trade-offs (latency, cost, risk, effort) in terms a product manager can act on.
• Stays calm and organized during incidents.
• Hires, grows, and retains engineers, and lets them own visible work.
• Bachelor's degree or higher in Computer Engineering, Computer Science, IT, or a related field.
• 6+ years in data engineering, including 2+ years leading a team as a team lead or technical lead of at least three engineers.
• Production ownership of both batch and streaming pipelines.
• Fluent Persian; English sufficient to read and write technical documentation.

Nice to have

• Banking, fintech, or another regulated data environment; regulatory or supervisory reporting.
• On-premises platforms at scale: capacity planning and cost control without elastic cloud.
• Data governance and cataloging.

Benefits:

• Work from home option
• Flexible working hours
• Training courses and professional development opportunities
• Military service project (Limited)
• Supplemental health insurance
• Team-building budget
• Performance-based bonuses
• Loans
• Lunch subsidies