Full‑Stack data engineer
Millennium · Bengaluru · 5+ yrs experience · Posted 2026-07-18
Tech stack: AWS, Angular, Elasticsearch, GitHub Actions, Jenkins, Python, SQL, Terraform
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About the role
We are seeking a highly self‑sufficient, motivated engineer based with strong full‑stack data engineering skills to join our innovative, dynamic team. This is a remote/offshore role that requires autonomy, excellent communication, and the ability to deliver high‑quality work with limited supervision while collaborating with a predominantly US‑based team. You will build reliable, scalable data products and user experiences that power AI/ML modeling, agentic workflows, and reporting—working end‑to‑end from data ingestion and transformation through to UI.
Responsibilities:
- Remote collaboration & communication:
- Operate effectively as an offshore member of a distributed team, proactively communicating status, risks, and blockers across time zones and coordinating overlap with US working hours as needed.
- Full‑stack data engineering:
- Build across the entire stack, including data ingestion/acquisition and transformation, APIs, front‑end components, and automated test suites, delivering production‑grade solutions with minimal hand‑holding.
- Autonomous delivery & ownership:
- Take end‑to‑end ownership of features and projects—clarifying requirements, breaking work into milestones, estimating timelines, and delivering high‑quality, well‑documented solutions.
- Specification and design:
- Translate short‑ and long‑term business requirements, architectural considerations, and competing timelines into clear, actionable technical specifications and design documents.
- Code quality: Write clean, maintainable, efficient code that adheres to evolving standards and quality processes, including unit tests and isolated integration tests in containerized environments.
- Continuous improvement: Contribute to agile practices and provide input on technical strategy, architectural decisions, and process improvements, continuously suggesting better tools, patterns, and automation.
Qualifications:
- 5+ years in software engineering, with a full‑stack background building complex, scalable data‑engineering pipelines using data warehouse technology, SQL with dbt, Python, AWS with Terraform, and modern UI technologies.
- Modern data engineering:
- Strong experience with medallion data architecture patterns using data warehouse technologies (e.g., Snowflake), data transformation tooling (e.g., dbt), BI tooling, and NoSQL data marts (e.g., Elasticsearch/OpenSearch).
- Testing and QA:
- Solid understanding of unit testing, CI/CD automation, and quality assurance processes for both data pipeline testing and operational data quality tests.
- Remote work & autonomy:
- Proven track record working in a remote or distributed environment, demonstrating self‑motivation, reliable execution, and the ability to make sound technical decisions independently.
- Agile methodology: Working knowledge of Agile development practices and workflows (e.g., sprint planning, stand‑ups, retrospectives) in a distributed team setting.
- Bachelor’s or Master’s degree in Computer Science, Statistics, Informatics, Information Systems, or a related quantitative field.
- Machine learning and AI:
- Hands‑on experience with large language models (LLMs) and agentic frameworks/workflows.
- Search and analytics:
- Familiarity with the ELK stack (Elasticsearch, Logstash, Kibana) for search and analytics solutions.
- Cloud expertise: Experience with AWS cloud services; familiarity with SageMaker; and CI/CD tooling such as GitHub Actions or Jenkins.
- Front‑end expertise: Experience building user interfaces with Angular or a modern UI stack.
- Financial domain knowledge:
- Broad understanding of equities, fixed income, derivatives, futures, FX, and other financial instruments.
Qualifications
- 5+ years in software engineering, with a full‑stack background building complex, scalable data‑engineering pipelines using data warehouse technology, SQL with dbt, Python, AWS with Terraform, and modern UI technologies.
- Modern data engineering:
- Strong experience with medallion data architecture patterns using data warehouse technologies (e.g., Snowflake), data transformation tooling (e.g., dbt), BI tooling, and NoSQL data marts (e.g., Elasticsearch/OpenSearch).
- Testing and QA:
- Solid understanding of unit testing, CI/CD automation, and quality assurance processes for both data pipeline testing and operational data quality tests.
- Remote work & autonomy:
- Proven track record working in a remote or distributed environment, demonstrating self‑motivation, reliable execution, and the ability to make sound technical decisions independently.
- Agile methodology: Working knowledge of Agile development practices and workflows (e.g., sprint planning, stand‑ups, retrospectives) in a distributed team setting.
- Bachelor’s or Master’s degree in Computer Science, Statistics, Informatics, Information Systems, or a related quantitative field.
- Machine learning and AI:
- Hands‑on experience with large language models (LLMs) and agentic frameworks/workflows.
- Search and analytics:
- Familiarity with the ELK stack (Elasticsearch, Logstash, Kibana) for search and analytics solutions.
- Cloud expertise: Experience with AWS cloud services; familiarity with SageMaker; and CI/CD tooling such as GitHub Actions or Jenkins.
- Front‑end expertise: Experience building user interfaces with Angular or a modern UI stack.
- Financial domain knowledge:
- Broad understanding of equities, fixed income, derivatives, futures, FX, and other financial instruments.
Responsibilities
- Remote collaboration & communication:
- Operate effectively as an offshore member of a distributed team, proactively communicating status, risks, and blockers across time zones and coordinating overlap with US working hours as needed.
- Full‑stack data engineering:
- Build across the entire stack, including data ingestion/acquisition and transformation, APIs, front‑end components, and automated test suites, delivering production‑grade solutions with minimal hand‑holding.
- Autonomous delivery & ownership:
- Take end‑to‑end ownership of features and projects—clarifying requirements, breaking work into milestones, estimating timelines, and delivering high‑quality, well‑documented solutions.
- Specification and design:
- Translate short‑ and long‑term business requirements, architectural considerations, and competing timelines into clear, actionable technical specifications and design documents.
- Code quality: Write clean, maintainable, efficient code that adheres to evolving standards and quality processes, including unit tests and isolated integration tests in containerized environments.
- Continuous improvement: Contribute to agile practices and provide input on technical strategy, architectural decisions, and process improvements, continuously suggesting better tools, patterns, and automation.