Lead Data Engineer

We are looking for a Lead Data Engineer at Flip. You will be responsible for designing, building, and managing data infrastructure and pipelines to support business intelligence, analytics, and possibly machine learning applications. In this role, you will also be expected to collaborate with Data Analysts to improve and enrich existing state of data to meet current as well as possible future use cases.

Key Responsibilities

• Design and implement scalable, reliable, and high-performance data architectures.

• Define data engineering best practices and strategies for data processing, storage, and retrieval.

• Ensure data architecture aligns with business needs and long-term company goals.

• Develop, test, and maintain ETL (Extract, Transform, Load) and ELT pipelines.

• Optimize data processing workflows for performance and cost-efficiency.

• Automate data ingestion from various sources (APIs, databases, streaming data, etc.).

• Lead and mentor a team of data engineers, ensuring best practices in coding and design.

• Collaborate with Data Analytics team and other data-driven departments (Fraud, Risk, etc.).

• Ensure data security, privacy, and compliance with regulations.

• Implement data lake and data warehouse solutions.

Requirements:

• 5+ years of experience in data engineering.

• Strong SQL skills and experience with data visualization tools (e.g., Redash, Looker, Power BI, Tableau).

• Proficiency in Python for data pipelining.

• Strong knowledge of GCP stacks (BigQuery, Datastream, Dataflow, Pub/Sub, etc).

• Strong knowledge of OLAP databases (BigQuery, Cassandra, duckDB).

• Experience with ETL/ELT tools like Apache Airflow and DBT.

• Able to design and implement Data Warehouse based on Google BigQuery.

• Effective communication skills for cross-team collaboration.

• Strong leadership and team management experience.

• Able to ingest new data structures into existing Data Warehouse.

Nice to Have

• Knowledge of open source stacks (Debezium, Kafka, duckDB).

• Experience in various ETL Tools (Pentaho, n8n, SSIS).

• Experience in Cube Data Warehouse Modelling.

• Knowledge in Google Cloud Platform stack costs and alternatives.