Lead Data Engineer

We are looking for a hands-on Data Engineer to build and scale the data infrastructure powering a high-volume, real-time mobility and logistics platform. You will work on designing reliable batch and streaming pipelines, building scalable data systems, and enabling teams across the organization to make better decisions through high-quality and accessible data. This role is ideal for someone who enjoys owning data systems end-to-end and working with large volumes of real-time operational data in a fast-paced environment.
What You'll Do
Design, build, and maintain scalable batch and real-time data pipelines.
Develop reliable data ingestion systems across multiple databases, services, and event streams.
Build and manage data warehouses, data lakes, and storage architectures for large-scale datasets.
Design pipelines supporting CDC, streaming, incremental syncs, schema evolution, deduplication, and backfills.
Work with engineering and business teams to understand data requirements and deliver robust data solutions.
Optimize SQL queries, storage, and pipeline performance for high-volume workloads.
Implement data quality checks, monitoring, alerting, reconciliation, and observability across pipelines.
Build cost-efficient data architectures capable of processing millions of events.
Own the complete lifecycle of data pipelines—from requirement gathering and design to deployment and monitoring.
Must have
Strong hands-on experience in Data Engineering and building production-grade data pipelines. Experience working with both batch and real-time/streaming data processing. Strong understanding of Kafka, Redpanda, CDC, and streaming architectures. Experience with ETL/ELT pipelines, data lakes, and data warehouses. Strong knowledge of relational and NoSQL databases such as PostgreSQL, MySQL, MongoDB, and ClickHouse. Experience with orchestration tools such as Airflow, Dagster, Prefect, or similar platforms. Strong SQL skills, including query optimization, window functions, aggregations, and performance tuning. Experience designing scalable data storage using technologies such as S3/GCS, Parquet, and partitioning strategies. Ability to independently take ownership of requirements, architecture, development, deployment, and monitoring. Experience implementing data quality checks, pipeline monitoring, and alerting.
Good to have
Experience working with high-volume logistics, mobility, marketplace, or hyperlocal platforms. Exposure to geospatial data and analytics. Experience with dbt and modern analytics engineering practices. Knowledge of Apache Iceberg, Delta Lake, or Apache Hudi. Experience with Kubernetes and containerized data workloads. Understanding of cost optimization for large-scale cloud data infrastructure. Experience working in a fast-paced startup or high-growth technology environment.

Work arrangement
Yes