About the job
In this role, you will bridge modern software engineering practices with cutting-edge data engineering and AI enablement. You will partner with client stakeholders and cross-functional teams to solve complex business challenges - architecting scalable pipelines, building robust cloud infrastructure, and integrating machine learning, Generative AI, and LLM solutions into production environments.
What You’ll Do
- Design, implement, and deploy bespoke data engineering solutions and scalable data services tailored to the specific needs and environments of our clients.
- Develop and optimize robust data pipelines.
- Implement cloud-native architectures for clients using AWS services (e.g., EMR, Glue, Kinesis/MSK, Lambda, S3, Redshift) and modern data warehouses/lakehouses (e.g., Redshift, Snowflake).
- Bridge the gap between client data foundations and artificial intelligence by building production-grade data pipelines for ML models, supporting MLOps, and implementing Generative AI / LLM applications to solve practical business problems.
- Establish processes and automated frameworks to monitor data quality, schema evolution, lineage, and security, ensuring client data assets are trusted, compliant, and auditable.
- Apply robust engineering standards to client codebase, including automated unit/integration testing, CI/CD pipelines, containerization, and Infrastructure as Code (IaC).
- Collaborate closely with client stakeholders, project managers, presales engineers, and data teams to translate business requirements into resilient technical designs.
- Provide guidance through architectural reviews, technical documentation, and code reviews, ensuring high delivery quality across client engagements.
What We’re Looking For
- Experience: 3+ years (Mid) or 5+ years (Senior) of hands-on software engineering or data engineering experience, preferably within a consulting, professional services, or project-based delivery environment.
- Strong proficiency in Python and solid backend development experience with Java for distributed data processing.
- Hands-on experience designing, provisioning, and maintaining data workloads on AWS (e.g., S3, Glue, Athena, EMR, Kinesis/MSK, Redshift, Lambda, IAM).
- Strong SQL proficiency and practical experience with data modeling (relational, dimensional, lakehouse architectures).
- Solid grasp of distributed systems concepts, concurrency, fault tolerance, data partitioning, and event-driven patterns.
- Strong analytical problem-solving abilities, self-sufficiency, and clear communication skills to collaborate effectively with both technical and non-technical client teams.
Nice to Have / Bonus Points
- Practical experience or strong interest in Generative AI, LLMs, vector search, or deploying machine learning pipelines in production (MLOps).
- Experience with Infrastructure as Code tools (e.g., Terraform, AWS CDK, CloudFormation).
- Exposure to multi-cloud environments (e.g., GCP in addition to AWS).
