Data Engineer Technical Lead

Responsibilities

• Lead, mentor, and grow a team of data engineers, fostering a culture of technical excellence, continuous learning, and collaboration;
• Provide technical oversight and architectural guidance across all data engineering projects, ensuring solutions are robust, scalable, and secure;
• Act as the primary technical point of contact for key client engagements, translating complex business requirements into actionable technical roadmaps;
• Drive the strategic direction of the team’s technical capabilities, particularly in areas of big data, streaming analytics, and MLOps;
• Contribute to pre-sales activities, including solution design, project scoping, and effort estimation for new client proposals;
• Hands-On Technical Delivery;
• Architect and implement end-to-end, cloud-native data solutions on AWS, leveraging services like Glue, EMR, Redshift, Kinesis, and SageMaker;
• Design, build, and optimize scalable ‘big data’ pipelines and data lakes that process diverse data types, from near real-time events to large unstructured files (images, documents, etc.);
• Lead the integration of AI/ML models into production data pipelines, working closely with data scientists to productionize algorithms and establish MLOps frameworks;
• Serve as the senior subject matter expert for clients and internal teams on database optimization (SQL/NoSQL), data governance, security, and cloud data architecture;
• Work hands-on with a wide variety of data sources and technologies, including Spark, Kafka, Elasticsearch, Python/Scala, and advanced SQL to solve challenging data problems.

Qualifications

• 5+ years of experience in data engineering, with at least 2 years in a formal or informal technical leadership or mentorship capacity;
• A Bachelor's or Graduate degree in Computer Science, Mathematics, or a related quantitative field;
• Expert-level proficiency with AWS data services (e.g., S3, Glue, EMR, Redshift, Athena, Kinesis, RDS, DynamoDB). AWS certification is a strong plus;
• Proven experience architecting and building production-grade data pipelines and data sets using big data technologies such as Spark, Kafka, or similar;
• Demonstrable experience implementing or supporting data solutions for AI/ML workloads (e.g., feature engineering pipelines, model deployment, MLOps);
• Deep knowledge of relational SQL (e.g., PostgreSQL, MySQL) and NoSQL databases (e.g., DynamoDB, Cassandra);
• Strong programming skills in Python, Java, or Scala;
• Extensive experience in a client-facing or consulting role, with exceptional communication and presentation skills;
• A natural leader, passionate about developing the skills and careers of team members;
• Fluency in English is essential.