Senior Software Developer

Key Responsibilities

• 🔹 Data Pipeline & Platform Development
• Design, build, and maintain scalable ETL/ELT pipelines that ingest and transform structured, semi-structured, and unstructured data
• Develop high-fidelity data pipelines on Databricks/Spark optimized for reliability, cost, performance, and data freshness
• Build curated datasets, embeddings-ready data, feature layers, and semantic abstractions that are AI/ML-consumable for downstream systems
• Implement ingestion, transformation, and serving layers across Data Lake / Lakehouse architectures with a focus on efficient retrieval and contextual usability
• 🔹 Data Modeling & Architecture
• Develop and maintain robust data models including fact/dimension models, SCDs, wide tables, and CDC pipelines
• Apply data versioning, incremental processing, partitioning, and clustering strategies to ensure consistency, reproducibility, and cost efficiency
• Contribute to architectural decisions and trade-offs across storage, compute, and orchestration layers within the analytics platform
• Help define and uphold data modeling standards, data contracts, and quality frameworks across teams
• 🔹 Analytics, AI & ML Enablement
• Prepare high-quality datasets for ML model consumption, feature engineering workflows, and predictive/forecasting use cases
• Contribute to a unified semantic layer that standardizes metrics, reusable definitions, and improves data access patterns
• Partner with Data Science teams to operationalize feature pipelines and support model training, serving, and monitoring
• 🔹 Quality, Governance & Reliability
• Implement data quality checks, contracts, and observability to ensure SLA/SLO adherence across pipelines
• Work with metadata, lineage, and data discovery frameworks to improve transparency, governance, and trust in data
• Drive improvements in pipeline reliability, monitoring, and incident response across the data ecosystem
• 🔹 Collaboration & Technical Leadership
• Partner cross-functionally with Product, Analytics, and Data Science to translate ambiguous business problems into reusable data assets
• Mentor junior engineers, review code/designs, and raise the bar for engineering quality and best practices
• Communicate technical decisions, trade-offs, and system designs clearly to both technical and non-technical stakeholders

Technical Skills

• Advanced proficiency in SQL and Python, with strong focus on query optimization, cost efficiency, and large-scale data processing
• Hands-on experience with Databricks, Apache Spark, and distributed processing frameworks
• Strong experience building ETL/ELT pipelines and workflow orchestration (Airflow, Dagster, or similar)
• Solid understanding of Data Lake / Lakehouse architectures, storage formats (Parquet, Delta/Iceberg), partitioning, clustering, and performance tuning
• Deep expertise in data modeling: fact/dimension models, SCDs, wide tables, CDC, data versioning, and incremental processing
• Experience with event-driven and streaming architectures (Kafka, Pub/Sub, Kinesis) applied pragmatically alongside batch where needed

Cloud & DevOps

Reliability, Observability & Governance

AI / ML & Analytics (Preferred)

Domain Knowledge (Preferred)

Soft Skills

• Proven ability to translate ambiguous business requirements into scalable data models and systems
• Ownership mindset — drives features end-to-end with strong communication and collaboration skills

Qualifications

• Bachelor's or Master's in Computer Science, Engineering, or related field — or equivalent practical experience
• 6–8+ years of experience in Data Engineering, building and operating production-grade data platforms at scale
• Demonstrated experience delivering data solutions across analytics, ML, or product-facing domains

This role will require you to be based in Bengaluru.