Technical Leadership & Strategy:
- Architect and implement enterprise-grade data platforms, including data lakes, warehouses, and real-time pipelines.
- Define and enforce data engineering standards, including ETL/ELT frameworks, data modeling, and quality controls.
- Lead performance optimization of databases, queries, and pipelines (e.g., partitioning, indexing, cost-efficient cloud solutions).
- Stay updated with emerging trends, evaluating and integrating new technologies.
Data Architecture & Infrastructure:
- Design and implement scalable data architecture that ensures high availability, security, and performance.
- Define best practices for data modeling, storage, retrieval, and governance.
- Optimize database structures, schemas, indexing, and partitioning for efficient query performance.
- Implement CI/CD for data pipelines and automate infrastructure using Infrastructure-as-Code (IaC) tools.
Data Pipelines & ETL Development:
- Architect and develop end-to-end ETL/ELT pipelines for structured and unstructured data ingestion, processing, and transformation.
- Automate and optimize data workflows using tools like Apache Spark, Kafka, Airflow, and dbt.
- Monitor, troubleshoot, and optimize data pipeline performance.
Data Governance, Security & Compliance:
- Implement data governance policies, including metadata management, lineage tracking, and role-based access control (RBAC).
- Ensure compliance with data privacy regulations (e.g., GDPR, CCPA) and enforce security best practices.
- Develop frameworks for data quality validation, anomaly detection, and integrity checks.
Business & Stakeholder Engagement:
- Collaborate with stakeholders (clients, PMs) to translate business needs into technical specifications and scalable architectures.
- Develop interactive dashboards and reporting solutions to provide actionable insights.
Innovation & Mentorship:
- Lead technical discussions, workshops, and knowledge-sharing sessions within the team.
- Mentor junior engineers, conduct code reviews, and set best practices.
- Drive automation of data workflows, including CI/CD for pipelines and Infrastructure-as-Code (IaC) solutions.
Technical Skills & Experience:
- 7+ years of experience in data engineering with a proven track record of building scalable data solutions.
- Expertise in SQL and relational database management systems (MySQL, PostgreSQL, Oracle).
- Proficiency in Python (or similar languages) for data processing and automation.
- Deep understanding of cloud platforms (AWS, GCP, Azure) and cloud-native data services:AWS (Redshift, Glue, S3)Azure (Synapse, Data Factory)GCP (BigQuery, Dataflow)
- Hands-on experience with big data tools:Apache Spark, Kafka, Airflow, dbt, Snowflake, Databricks
- Expertise in data modeling.
- Strong knowledge of data security, governance, and compliance frameworks.
Leadership & Soft Skills:
- Proven track record of leading data projects, including migrations, warehousing, and real-time analytics.
- Ability to lead cross-functional teams and manage client expectations.
- Strong problem-solving skills for debugging distributed systems and resolving data anomalies.
- Experience with Agile/Scrum methodologies, including tools like Jira, Git.
- Excellent communication and stakeholder management skills.
Nice-to-Have (Bonus Skills):
- Certifications in cloud/data technologies (e.g., AWS Certified Data Analytics, Google Cloud Data Engineer).
- Experience with ML pipelines (e.g., feature stores, model deployment).
- Contributions to open-source data projects.