Job Description

Role: Data Engineer

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Location: Bangalore

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Department: Platform Engineering / Data Platform

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Experience: 3–8 Years

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About Styli Marketplace

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Launched in 2019 by Landmark Group, Styli Marketplace is the first e-commerce venture of the group, quickly becoming a leading online destination for fashion and lifestyle across the GCC, including Saudi Arabia, the UAE, Kuwait, Bahrain, and beyond. Styli connects global sellers and creators with millions of fashion-forward customers, offering the latest trends, exceptional value, and convenient services like same-day to 48-hour delivery and flexible payment options. Our mission is to make style accessible, aspirational, and exciting for all, backed by a passionate team fostering a culture of creativity and innovation. At Styli, we aim to revolutionize fashion retail and bring unique experiences to our customers.

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Role Overview

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We are looking for a skilled and curious Data Engineer to join our Data Platform team. You will design, build, and maintain the pipelines, data models, and infrastructure that turn raw e-commerce data into trusted, analytics-ready assets. This is a hands-on role with high ownership — you will work closely with data analysts, ML engineers, and product teams to ensure the right data is available at the right time with the right quality.

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What You‘ll Do

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Data Pipeline Development

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- Design, build, and maintain batch and real-time data pipelines that ingest data from transactional systems, third-party APIs, event streams, and operational databases.
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- Build robust, idempotent, and observable ETL/ELT workflows using Apache Airflow, dbt, or equivalent orchestration and transformation tooling.
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- Handle ingestion from diverse sources: MySQL/PostgreSQL, Kafka event streams, S3/GCS data lakes, REST APIs, and SaaS platforms.
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- Ensure pipelines are fault-tolerant, recoverable, and instrumented with data quality checks at every stage.
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Data Modelling & Warehouse Engineering

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- Design and maintain dimensional and analytical data models (star schema, OBT, wide tables) optimized for BI reporting and ML feature consumption.
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- Own the data warehouse layer on BigQuery — including schema design, partitioning strategies, clustering, and cost-efficient query patterns.
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- Write and review dbt models, tests, documentation, and lineage to maintain a well-governed transformation layer.
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- Collaborate with analysts to translate business questions into clean, reusable, and well-tested data models.
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Streaming & Real-Time Data

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- Build and maintain real-time data pipelines for use cases such as live inventory updates, order event processing, and customer behaviour streams.
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- Implement stream processing logic using Apache Flink, Spark Streaming, or ksqlDB for low-latency data delivery.
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- Design Kafka topic schemas, partition strategies, and consumer group management for high-throughput e-commerce event flows.
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Data Lake & Cloud Infrastructure

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- Build and manage a well-structured data lake on GCS
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- Work with cloud-native data services:
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- Collaborate with the Platform/DevOps team on data infrastructure provisioning using Terraform and containerized data workloads on Kubernetes.
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Data Quality & Observability

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- Implement data quality frameworks using Great Expectations, dbt tests, or Monte Carlo to catch anomalies, nulls, duplicates, and schema drift before they reach downstream consumers.
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- Build and maintain data observability dashboards — pipeline SLAs, freshness SLOs, row count anomalies, and schema change alerts.
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- Own incident response for data pipeline failures, with transparent runbooks and escalation paths.
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ML & Analytics Enablement

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- Build and maintain feature pipelines for ML models powering personalisation, recommendations, fraud detection, and demand forecasting.
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- Partner with ML engineers to design feature stores (Feast or equivalent) and ensure training/serving consistency.
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- Enable self-serve analytics by maintaining clean semantic layers and well-documented data marts for business intelligence tools (Looker, Metabase, Superset).
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What We‘re Looking For

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Required

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- 3–8 years of hands-on data engineering experience, ideally in a high-transaction e-commerce, fintech, or consumer tech environment.
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- Strong proficiency in SQL — complex analytical queries, window functions, CTEs, query optimisation, and warehouse-specific dialects (BigQuery / Redshift).
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- Hands-on experience with Apache Airflow for pipeline orchestration and dbt for transformation layer management.
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- Experience building and operating data pipelines at scale — handling millions of events per day with reliability and observability.
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- Working knowledge of Apache Kafka or equivalent event streaming platforms for real-time data ingestion.
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- Experience with cloud data warehouses: BigQuery (preferred) and/or AWS Redshift / Athena.
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- Proficiency in Python for pipeline development, data transformation, and automation.
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- Hands-on experience with cloud platforms — GCP (BigQuery, Cloud Composer, Dataproc) and/or AWS (Glue, Athena, Kinesis, S3).
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- Understanding of data modelling principles: normalisation, dimensional modelling, and analytical patterns.
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Good to Have

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- Experience with open table formats: Apache Iceberg, Delta Lake, or Apache Hudi.
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- Familiarity with Apache Spark (PySpark) for large-scale distributed data processing.
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- Exposure to feature store design and ML pipeline integration.
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- Experience with data cataloguing and governance tooling (DataHub, Amundsen, Collibra, or Alation).
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- Knowledge of data mesh or data product principles and federated ownership models.
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- Experience with real-time analytics databases: ClickHouse, Druid, or Pinot for high-throughput OLAP workloads.
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- Familiarity with infrastructure-as-code (Terraform) and deploying data workloads on Kubernetes.
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- Relevant certifications: Google Professional Data Engineer, AWS Data Analytics Specialty, dbt Analytics Engineering.
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The Data Problems You‘ll Solve at Styli

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- Personalisation at scale — building event pipelines that capture every click, view, and purchase to power real-time recommendations for millions of customers.
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- Inventory & demand forecasting — reliable pipelines feeding ML models that optimise stock levels across hundreds of SKUs and multiple markets.
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- Campaign & marketing analytics — ingesting data from paid channels, CRM, and app attribution to give growth teams a single source of truth.
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- Order & payments intelligence — real-time event streams for order lifecycle tracking, fraud signals, and settlement reconciliation.
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- Cross-market reporting — unified data models serving multiple country markets with different currencies, catalogues, and fulfilment providers.
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📌 Data Engineer 2 (Bengaluru)
🏢 STYLI
📍 Bengaluru