Job Objective

We are building a governed, self-service data platform that enables teams to create and document their own analytics while maintaining a single source of truth for company-wide metrics.

You will join the data team to build and operate the core data platform. This is an infrastructure-first role with minimal stakeholder-facing responsibilities. You will not be responsible for requirements interview, end-user support, or cross-team coordination. Success is measured by technical execution: platform reliability, performance, correctness, automation, and adherence to engineering best practices.

Evaluation is based on technical outcomes: reliability, performance, correctness, automation, and adherence to engineering best practices.

Key Responsibilities

• Design and operate production-grade data ingestion (batch and streaming/CDC).

• Build scalable transformation layers with clear modeling, incremental processing, and reproducible backfills.

• Implement testing and data quality gates and support their enforcement through tooling and automation.

• Automate documentation and maintain clear, versioned platform standards (schemas, conventions, runbooks).

• Tune and scale ClickHouse and PostgreSQL for OLAP performance, concurrency, and cost-efficiency.

• Implement and operate platform observability (metrics, dashboards, alerting) and reduce incidents through preventive engineering.

• Operate and maintain streaming infrastructure: Kafka, Kafka Connect, and Debezium (CDC), including stability and upgrades.

• Build and maintain platform CI/CD with GitLab CI/CD and strong release hygiene.

• Maintain engineering quality through code review, documentation, and adherence to defined architectural standards.

•   Responsibilities are shared across the data platform team; no single engineer is expected to own all components end-to-end.

 Role Complexity

This role is intentionally structured to maximize time spent on building and operating the data platform:

• Low meeting load / low context switching. The Data Platform Lead handles most cross-team coordination and intake.

• You focus on engineering outcomes. Your time goes into implementing platform architecture, reliability, performance, quality controls, and automation.

• Written interfaces over meetings. Standards, contracts, documentation, and tooling are the primary way teams consume the platform.

In this role, you will have:

• Real technical influence: You will contribute to architectural decisions and lead implementation of agreed designs, under the direction of the Data Platform Lead.

• Few distractions: minimal stakeholder management; strong focus time for deep engineering.

• Hard problems, production scale: performance tuning, operational reliability, streaming/CDC, and automation at scale.

Requirements

• +5 years of experience as a Data Engineer / Data Platform Engineer.

• Proven track record of delivering high-reliability, high-scale pipelines and platform components in production.

• Practical experience tuning large-scale ClickHouse and PostgreSQL databases.

• Experience handling large datasets using PySpark for batch processing.

• Ability to operate and maintain a production Kafka setup including Kafka Connect and Debezium (CDC).

Skills

• Expert SQL and query optimization, with the ability to reason about execution plans and performance trade-offs.

• Strong Python for production-grade data systems (not notebooks-only).

• Hands-on experience with Airflow (or equivalent orchestration) in production.

• Practical, deep experience with ClickHouse and PostgreSQL, including tuning and scaling at high volume.

• Monitoring/alerting experience using Grafana (or equivalent) to ensure system health.

• Strong understanding of data engineering architecture: idempotency, retries, lineage, partitioning, SLAs/SLOs, backfills.

• Production experience with Kafka ecosystems: brokers, topics, consumer groups, lag, Connect, Debezium.

• Familiarity with Kubernetes.

• Strong code review habits and documentation standards.

Preferred Skills

• Experience with object storage tooling like MinIO (S3-compatible) and lake-style patterns.

• Experience designing governed self-service systems: templates, guardrails, contracts, standardized layers.

Tech Stack (Current / Expected)

• Orchestration: Airflow

• Storage/Compute: ClickHouse, PostgreSQL

• Streaming/CDC: Kafka, Kafka Connect, Debezium

• Object Storage: MinIO

• Platform: Kubernetes, GitLab CI/CD

• Observability: Grafana

·        Primary programming language: Python

Benefits & Perks

• Location: Miare Headquarters – Sharif Innovation Station (Tarasht, Tehran)

• Full-time, on-site position

• Competitive compensation aligned with senior-level IC impact and market benchmarks.

• Flexible hours