Senior Software Engineer - Data

Wishlink; Gurugram District; Presencial; INR 4.000.000 - 7.000.000; Jornada completa

Descripción de la vacante

Wishlink is seeking a Senior Software Engineer (Data) to design and build production-grade data pipelines from event collection through storage and serving, owning the end‑to‑end data lifecycle with high reliability and performance.

You will work with Spark, Databricks, Delta Lake, BigQuery, Airbyte and Power BI, focusing on data infrastructure with a clear path to broader platform ownership while collaborating closely with analytics and product teams to translate complex data needs into

Formación

• 5+ years of software engineering experience with at least 2+ years in scaling data infrastructure.
• Strong backend, distributed systems and cloud (AWS/GCP) expertise.
• Focus on data quality, reliability, and end-to-end data lifecycle.
• Ability to guide peers in best practices, code quality, and architecture discussions.

Responsabilidades

• Build production code for data pipelines from event collection to storage and serving.
• Architect and optimize end-to-end data lifecycle with high reliability and performance.
• Lay groundwork for a self-serve analytics platform for product and business teams.
• Build observability systems to surface pipeline failures and data issues.
• Evaluate AI-native approaches to improve quality checks and monitoring.
• Collaborate with analytics and product teams to translate data requirements into engineering work.
• Initial focus on data infra with potential to own broader platform challenges.

Conocimientos

Senior Software Engineer

Data infrastructure

Backend engineering

Distributed systems

Cloud platforms (AWS/GCP)

Data quality & reliability

Code reviews & architecture

Communication skills

Herramientas

Spark

Databricks

Delta Lake

BigQuery

Airbyte

Power BI

Descripción del empleo

Senior Software Engineer (Data)

What would you be doing

• Watch Reels and YouTube videos every day level up on your fashion sense!
• This is a hands‑on engineering role where you will actively build, write production code and not just manage or delegate.
• Architect, build, and optimize data pipelines from event collection to storage and serving, owning the end‑to‑end data lifecycle with high reliability and performance.
• Lay the foundational groundwork for a self‑serve analytics platform where product and business teams can independently derive insights with minimal engineering dependency.
• Build observability systems that proactively surface pipeline failures, data staleness, and anomalies before they impact stakeholders.
• Evaluate and implement AI‑native approaches (LLM‑assisted quality checks, automated monitoring, natural language access) where they deliver practical improvements.
• Work closely with analytics and product teams to translate complex data requirements into well‑scoped engineering execution.
• While your initial focus will be on data infrastructure, there is a clear path to owning broader platform challenges as the team grows.
• Navigate our current stack: Spark, Databricks, Delta Lake (Medallion architecture), BigQuery, Airbyte, and Power BI.

What are we looking for

• If you are a content creator yourself or have worked closely in the social media space, we like you already :)
• 5+ years of software engineering experience, with at least 2+ years deeply involved in scaling data infrastructure. You identify as a strong software engineer first, not just an operator of ETL tools.
• Strong foundation in backend engineering, distributed systems, and cloud platforms (AWS/GCP), paired with a deep understanding of modern data concepts like Lakehouse architectures, data modeling, and batch/streaming processing.
• You care about data quality and reliability as measurable outcomes: pipeline freshness SLAs, schema drift detection, row‑count anomalies, and query performance. You've built or operated systems that track these.
• Proven ability to guide peers on engineering best practices, code quality, and scalable system design through code reviews and architecture discussions, without needing formal authority.
• Strong communication skills. You can translate between data engineering and business/analytics stakeholders, making complex tradeoffs understandable to non‑technical teams.

Good to Have

• Experience with CDC pipelines and streaming ingestion tools like Debezium, Kafka connectors, or Airbyte.
• Hands‑on experience with data partitioning, sharding strategies, and columnar databases like BigQuery for analytical workloads.
• Familiarity with BI tooling and building data models that serve non‑technical users directly.