Data Engineer 2

Role: Data Engineer

Location: Bangalore

Department: Platform Engineering / Data Platform

Experience: 3–8 Years

About Styli Marketplace

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.

Role Overview

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.

What You'll Do

Data Pipeline Development

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

Data Modelling & Warehouse Engineering

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

Streaming & Real-Time Data

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

Data Lake & Cloud Infrastructure

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

Data Quality & Observability

• 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.
• Build and maintain data observability dashboards — pipeline SLAs, freshness SLOs, row count anomalies, and schema change alerts.
• Own incident response for data pipeline failures, with clear runbooks and escalation paths.

ML & Analytics Enablement

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

What We're Looking For

Required

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

Good to Have

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

The Data Problems You'll Solve at Styli

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