Job Description:
About the Role
We are looking for a Data Engineer to join our data engineering team, building and maintaining the platforms that ingest, transform, and serve data for our media and sustainability analytics products. You will work on self-serve data platforms used by internal teams and clients to connect data sources, apply business logic and taxonomies, and deliver clean, trusted data into reporting and analytics tools. This is a hands-on engineering role where you'll take ownership of well-scoped components while working closely with senior engineers on broader architectural decisions.
What You'll Do
Build and maintain data pipelines that ingest data from third-party APIs and internal sources into cloud data lake and lakehouse environments
Develop data transformation logic (Spark/PySpark, SQL) to standardize, model, and enrich raw data into analytics-ready datasets
Build and maintain orchestration workflows to schedule, monitor, and troubleshoot data pipeline execution
Support data governance and access control models (e.g. Unity Catalog, ABAC-based policies) to help ensure data is secure and appropriately scoped by tenant, client, or market
Work with product managers and senior engineers to implement platform features such as connector frameworks, taxonomy/rules engines, and data export capabilities
Support integration with visualization and reporting tools (e.g. Power BI, Tableau) and help ensure downstream data consumers have reliable, well-documented access
Contribute to architecture documentation (e.g. C4 model diagrams) and participate in design reviews
Troubleshoot data quality, pipeline failures, and performance issues, tracing errors from source to destination
Work with DevOps/security teams on service account management, credential handling, and infrastructure migrations (e.g. containerization)
Participate in on-call/support rotations as needed for production data pipelines
What You'll Bring
3+ years of experience as a Data Engineer building production-grade data pipelines
Solid hands-on experience with Apache Spark (PySpark) and SQL for data transformation at scale
Experience with cloud platforms (Azure preferred) and cloud-native data storage (e.g. Data Lake / Blob Storage)
Experience with Databricks, including familiarity with Unity Catalog or similar data governance/catalog tools
Familiarity with data governance and access control models (RBAC/ABAC), and working with sensitive, multi-tenant data
Experience integrating data pipelines with BI/visualization tools (Power BI, Tableau, or similar)
Comfortable working with API-based data ingestion tools/connectors (e.g. Adverity or similar ingestion platforms) is a plus
Solid understanding of software engineering practices: version control, CI/CD, testing, code review
Good communication skills and ability to work cross-functionally with product, engineering, and client-facing stakeholders
Nice to Have
Experience with workflow orchestration tools such as Apache Airflow
Experience with identity/access management integrations (Okta, Entra ID)
Experience with service mesh technologies (Istio) and containerized deployments (AKS/Kubernetes)
Exposure to sustainability, ESG, or carbon accounting data models
Experience with C4 model architecture documentation (PlantUML or similar
Location:
DGS India - Bengaluru - Manyata N1 Block
Brand:
Merkle
Time Type:
Full time
Contract Type:
Permanent