This is Hybrid role .Job Locations:

Bengaluru, India / Noida, India / Pune, India / Gurgaon, India / Mumbai, India

Skills Required

Primary Skills

Databricks and PySpark; Advanced SQLmedallion/lakehouse; Data vault 2.0

Job Description

Key Responsibilities

Own end-to-end technical design of lakehouse pipelines and frameworks: ingestion patterns, medallion layer standards, metadata-driven frameworks, reusable libraries, and naming/coding conventions.

Lead and grow a squad of data engineers (typically 5 10): task breakdown and estimation, sprint planning with the Scrum Master/EM, code reviews, pairing, and performance feedback.

Translate business and architecture requirements into technical designs and delivery plans; present and defend design decisions and trade-offs to QBE architects and platform owners.

Set and enforce engineering quality gates: PR review standards, unit/integration test coverage for pipelines, data quality SLAs, CI/CD promotion criteria, and documentation.

Own non-functional outcomes performance, cost optimisation (cluster/DBU governance), security (Unity Catalog permissions, PII handling), reliability, and observability of the platform.

Manage technical risk: dependency tracking, proof-of-concepts for new patterns (streaming, DLT, Asset Bundles), remediation plans for tech debt, and production incident command for critical issues.

Coordinate across workstreams Data Modelers, BDAs, testing, governance to keep specs, models, code, and test coverage in lockstep; run design authority sessions.

For the Onshore Lead: front-door for client stakeholders requirement workshops, steering-committee technical inputs, escalation handling, and onshore offshore handshake quality.

For the Offshore Lead: run offshore ceremonies, own sprint delivery and status, ensure overlap-hours coverage, and manage the offshore onshore work packaging.

Must-Have Skills & Experience





12+ years in data engineering with 5+ years leading teams delivering on Spark platforms; deep, current hands-on Databricks + PySpark (this is a coding lead, not a pure people-manager role).

Proven architecture-level command of the lakehouse/medallion pattern, Delta Lake, Unity Catalog, and Azure data services (ADLS Gen2, ADF, Key Vault, networking basics for Databricks).

Track record designing metadata/config-driven ingestion and transformation frameworks used by multiple teams.

Advanced Spark performance engineering and cost governance at platform scale.

Strong SDLC leadership: Git branching strategy, CI/CD (Azure DevOps/GitHub Actions), environment strategy, release management.

Insurance domain experience P&C; strongly preferred (policy, claims, premium, reinsurance, actuarial data flows).

Experience running distributed onshore/offshore delivery models with measurable quality outcomes.

Good-to-Have

DLT, Databricks Asset Bundles, Terraform for Databricks/Azure.

Streaming architectures (Auto Loader, Structured Streaming, Kafka/Event Hubs).

Migration experience (on-prem/legacy ETL Databricks); Informatica/DataStage/SSIS conversion.

Databricks Professional certification; Azure Solutions Architect (AZ-305) or Data Engineer (DP-203/DP-700).

Qualifications

Bachelor s/Master s in Computer Science, Engineering, or related field.

Qualified & Communication Skills

Executive-ready communication can hold the room with client architects and translate for business stakeholders.

Decision-making with documented trade-offs; comfortable saying no with alternatives.

Mentoring culture-builder; raises the squad s bar rather than becoming the bottleneck.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Data Engineering Lead (Databricks / PySpark) (Uttar Pradesh)
🏢 Sparix Global
📍 Uttar Pradesh