🌟 We're Hiring: Data Technical Lead! 🌟

We are seeking an experienced and driven Data Technical Lead to spearhead our data initiatives. The ideal candidate will bring deep expertise in data engineering, architecture, and analytics to deliver robust solutions and guide our technical teams. If you thrive in a dynamic environment and excel at leading high-impact projects, we'd love to meet you!

📍 Location: Mumbai, India
⏰ Work Mode: Work from Office
💼 Role: Data Technical Lead

What You'll Do:

Own technical design and delivery for squads; break work into clear, testable increments.
Guide modeling and pipeline patterns (ELT/streaming), performance, and reliability.
Review code, enforce standards (testing, CI/CD, security), and drive automation.
Mentor engineers; pair, coach, and develop growth plans and best practices.
Coordinate with Architects/Product/PMs to align scope, risks, and trade-offs.
Troubleshoot production issues; lead incident/post-mortem and preventive actions.
Maintain technical documentation, runbooks, and reusable templates/modules.

What We're Looking For:
✅ Minimum 8 years of hands-on experience in data engineering or related fields

Strong hands-on in SQL and Python for data engineering.
Solid data warehousing/lakehouse modeling and pipeline design (batch/stream).
Proven experience with orchestration and CI/CD for data workloads (Git-based).
Working knowledge of data quality, testing, and observability practices.
Cloud data platforms exposure (Azure/AWS/GCP) and container/orchestration basics.
Clear communication, leadership, pragmatism, and ownership in cross-functional teams.

NICE-TO-HAVE

Tools: dbt, Airflow/Dagster, Spark/Databricks, Kafka, Snowflake/BigQuery/Redshift.
IaC/DevOps (Terraform, Helm) and GitOps; SRE practices (SLIs/SLOs).
Security & governance awareness (RBAC, secrets, catalogs/lineage).
Experience building multi-tenant platforms and guiding cost/performance
optimizations.

Compétences requises

Data Engineering (hands-on)SQL for data engineeringPython for data engineeringData warehousing / lakehouse modelingPipeline design (ELT, batch and streaming)Orchestration and CI/CD for data workloads (Git-based)Data quality, testing and observability practicesCloud data platforms and container/orchestration basics (Azure/AWS/GCP)Code review, testing/security standards enforcement and automationTeam leadership, mentoring and coachingIncident management, troubleshooting and post-mortem leadershipdbt (data transformation tooling)Airflow / Dagster (orchestration tools)Spark / Databricks (big data processing)

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