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Senior Data Engineering Leader
A senior data engineering leader capable of driving data modernization, transformation, governance and analytics consumption initiatives while leading teams and delivering scalable cloud-native data platforms.
Responsibilities
- Build and manage scalable ETL/ELT pipelines for structured, semi-structured, and unstructured data.
- Develop batch and real-time data processing solutions using modern cloud and big data technologies.
- Design and implement data models, data marts, and analytics-ready datasets for reporting, BI, AI, and ML use cases.
- Drive cloud data engineering initiatives across AWS, Azure, or GCP environments.
- Ensure data quality, governance, lineage, security, and compliance across the data ecosystem.
- Collaborate with Business, Data Architects, Data Scientists, and BI teams to deliver business value through data products.
- Lead modernization of legacy data warehouses and support data lakehouse architectures and platform transformations.
- Establish engineering best practices, CI/CD pipelines, Infrastructure as Code (IaC), and automation standards.
- Mentor and lead Data Engineers while conducting architecture reviews and technical governance.
- Optimize platform performance, scalability, reliability, and cloud costs.
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Core Technical Skills
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- SQL, Python, PySpark, Spark
- Data Modeling (Star Schema, Snowflake, Data Vault)
- Airflow, DataStage, Informatica, Talend, DBT
- BigQuery, Snowflake, Synapse, Redshift
- Kafka, Pub/Sub, Event Hub
- Git, CI/CD, Terraform, Docker, Kubernetes
- Cloud Platforms: AWS, Azure, GCP
Preferred Skills
- Data Lakehouse (Databricks, Delta Lake)
- Master Data Management (MDM)
- Data Mesh/Data Product Architecture
- AI/ML & GenAI Data Platforms
- BFSI, Insurance, Retail, or Healthcare domain experience.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
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