Build and maintain cloud data pipelines, transformation workflows, orchestration, governance, and access controls for analytics products. Integrate APIs, Databricks, BI tools, and multi-tenant data platforms while troubleshooting quality and performance issues. Collaborate with product, engineering, DevOps, security, and client-facing teams on platform features, architecture documentation, infrastructure migrations, and production support.
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 BlockBrand:
MerkleTime Type:
Full timeContract Type:
PermanentSimilar Jobs
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Designs and supports batch and incremental data ingestion pipelines, ETL workflows, SQL transformations, and Airflow orchestration. Investigates data quality issues, reconciles discrepancies, performs data lineage analysis, supports cloud migration initiatives, and validates data across systems. The role also provides production support, resolves incidents, develops reusable engineering components, and collaborates with stakeholders on data requirements.
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Designs and supports batch and incremental data ingestion pipelines, ETL workflows, SQL transformations, BigQuery data platforms, and Airflow orchestration. Investigates data quality issues, validates data across systems, performs lineage and source-to-target analysis, supports cloud migration initiatives, and resolves production incidents. Collaborates with stakeholders and engineering teams to define data requirements and develop reusable data engineering components.
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Design and support batch and incremental data ingestion pipelines, ETL workflows, SQL transformations, and Airflow orchestration. Troubleshoot data quality, reconciliation, lineage, discrepancies, and production incidents. Support cloud data migrations, validate data across systems, collaborate with stakeholders, and develop reusable engineering components using BigQuery, SQL, Python, Airflow, and GCP services.
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