Forward Deployed Engineer — Data Engineering & Infrastructure

Location: Bengaluru (In-office)

Type: Full-time | 5 Days a Week

Experience: 5–8 Years (preferably in a client-facing or embedded context)

About Bluecopa

Bluecopa exists to bring finance into the next age of tooling and eventually make it autonomous. We’ve built a product that has found acceptance with big enterprises. Our platform helps finance teams streamline operations, automate reconciliations, and gain real-time business insights.

The Role

You will sit at the intersection of engineering and client success — embedded with customers to understand their data problems deeply, then design and ship the infrastructure to solve them. This is not a support role.

You will architect data pipelines, own infrastructure decisions on-site, and translate ambiguous business requirements into production-grade, idempotent systems. You will make calls on what gets built custom versus what gets pushed back into the core platform, and feed those insights directly to the product team.

The role works closely with Implementation and Product to assess platform gaps, and with clients from pre-sales through go-live. Expect equal parts terminal, architecture whiteboard, and client call.

Key Responsibilities

• Embedded Delivery: Embed with enterprise clients to scope, design, and deliver data engineering solutions end-to-end.
• Pipeline Engineering: Build and maintain production-grade data pipelines — both batch and streaming — on data warehousing and distributed infrastructure.
• Pipeline Integrity: Design and enforce idempotent, re-playable processing patterns across ingestion and ETL workflows to guarantee exactly-once semantics and safe re-runs.
• Infrastructure Provisioning: Own end-to-end cloud infrastructure configuration: GCS bucket policies, VM sizing/lifecycle management, GKE pod specs, Kubernetes manifests, and cluster-level resource management.
• Workflow Orchestration: Configure and extend workflow orchestration using Bluecopa's native constructs to model complex client automation requirements.
• Technical Design: Translate Statements of Work (SOW) and business requirements into detailed technical designs and implementation plans.
• Strategic Triaging: Make strategic decisions on whether to request a core platform feature or build a custom client implementation.
• Client Advisory: Lead deep technical discussions with enterprise clients during pre-sales and implementation phases.
• Product Feedback: Act as the technical voice of the customer — document patterns, pain points, and product gaps to share with internal tooling and architecture review boards.

Required Qualifications

Technical Requirements

• BigQuery Depth: Deep familiarity with BigQuery — query optimization, schema design, partitioning, clustering, and cost management.
• Pipeline Mastery: Strong command of ETL/ELT pipeline design — including idempotency, re-playability, checkpointing, and failure recovery strategies.
• GCP Infrastructure: Hands-on experience provisioning and managing GCP infrastructure: GCS, Compute Engine VMs, GKE clusters, Kubernetes pods, deployments, and namespaces.
• Distributed Systems: Solid understanding of distributed systems fundamentals — consistency, fault tolerance, exactly-once processing, and scalability patterns.
• Workflow Engines: Experience with workflow engines (Temporal, Argo, Airflow, or similar) — durable execution, retries, and DAG-based orchestration in production.
• Core Code: Command of Python and SQL for pipeline development, data transformation, and automation scripting.
• Containers & APIs: Familiarity with container-based deployments (Docker, Kubernetes manifests, Helm charts) and REST/GraphQL API integration patterns.
• Infrastructure as Code (IaC): Comfort reading and writing IaC (Terraform or equivalent) is a strong plus.

Client Alignment & Product Thinking

• Comfort with Ambiguity: Ability to drive technical clarity from loosely defined requirements.
• Bilingual Communicator: Strong written and verbal communication — able to navigate a CTO and a data analyst in the same conversation.
• Product Instinct: Thinks about edge cases, long-term maintainability, and platform reuse — not just task completion.
• Consulting Maturity: Experience working directly with enterprise clients or in a forward-deployed / embedded engineering context.

Preferred Qualifications (Nice to Have)

• Experience in a startup or fast-paced consulting / professional services environment.
• Exposure to LLM tooling or AI-native product development.
• Familiarity with data contracts and schema registries.
• Technical background in fintech or enterprise SaaS platforms.
• Prior experience as a solutions engineer, data platform engineer, or infrastructure lead.

What We Offer

• Competitive compensation package
• Opportunity to work with cutting-edge distributed data ecosystems
• High ownership, autonomy, and direct visibility with enterprise leadership
• Professional development and rapid career growth within a scaling SaaS ecosystem
• Collaborative, innovation-first work environment
• Comprehensive health and wellness benefits