Data Engineer (SDE 2)
Bengaluru
- Pay
- Pay not listed
- Work setting
On-site — work setting source
Work setup in the employer’s posting: onsite
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s posting: Fulltime- Office
From the employer’s posting
What you’ll work on
Full postingManage complex CDC flows from production databases into the lakehouse
Optimize distributed query engines for the data platform
Prepare TRDs and take part in design reviews for key tech initiatives
Design OLAP domain models such as facts, dimensions, SCDs and OBT tables
Work with product and key stakeholders to deliver data and analytics for business workflows
From the employer’s posting
manage complex CDC flows
optimize distributed query engines
Drive key tech initiatives by preparing TRD and actively involve in design reviews
Should be hands on in designing Domain models for OLAP like Fact, Dimension, Cumulative , types of SCD’s and OBT pattern tables
Interact with the Product & Key Stakeholders & help them by adding value to the business workflow with data & analytics
What you’ll bring
All qualificationsCore experience
- 3–5 years in data engineering with distributed and cloud-native systems
- Expert-level Python/PySpark and SQL
- Hands-on AWS (S3, EKS, MSK) and Infrastructure-as-Code
- Experience with Airflow or Temporal workflow orchestration
- Ability to explain trade-offs between storage formats and processing frameworks
Qualification wording
3–5 years in Data Engineering, specifically with distributed systems and cloud-native architectures
Expert-level Python/PySpark and SQL
Hands-on experience with AWS (S3, EKS, MSK) and Infrastructure-as-Code
Experience with Airflow or Temporal for complex workflow management
Ability to explain the trade-offs between different storage formats and processing frameworks
Tools in this posting
- Java
- Python
- Scala
- AWS
- BigQuery
- ClickHouse
- Grafana
- Iceberg
- Kafka
- Kubernetes
- Metabase
- Power BI
- S3
- Spark
- Superset
- Tableau
- Terraform
- Trino
- Airflow
- PySpark
- SQL
- Go
Source — Tool mentions in context
- Experience: 3–5 years in Data Engineering, specifically with distributed systems and cloud-native architectures. - Coding: Expert-level Python/PySpark and SQL. Familiarity with Go/Java/Scala is a plus - Infrastructure: Hands-on experience with AWS (S3, EKS, MSK) and Infrastructure-as-Code.
- Coding: Expert-level Python/PySpark and SQL. Familiarity with Go/Java/Scala is a plus - Infrastructure: Hands-on experience with AWS (S3, EKS, MSK) and Infrastructure-as-Code. - Orchestration: Experience with Airflow or Temporal for complex workflow management.
- Unified Compute: Developing robust ETL/ELT frameworks in PySpark and Flink (handling both batch and streaming workloads). - Infrastructure & Ops: Managing data workloads on AWS (EMR, EKS, MSK, S3) and automating everything via Gitlab/Github Actions. - Query & BI: Tuning Trino or Clickhouse to power real-time dashboards in Metabase, Superset, and PowerBI.
- Ingestion & CDC: OLake and PeerDB for near real-time sync from production systems into Alchemy; Debezium/Kafka for CDC-heavy use cases, with support for Kafka and S3-based sources. - Lakehouse / Storage: Alchemy on Apache Iceberg with S3 as the data lake storage layer and AWS Glue Catalog for metadata; exposure to Delta/Hudi is a plus. - Processing & Compute: PySpark on EMR/EKS for batch and streaming workloads; Flink and Spark Structured Streaming fundamentals for low-latency pipelines.
- Data Quality & Governance: DQ checks, freshness/SLA monitoring, source-to-lake reconciliation, deduplication, schema evolution handling, and cataloging/lineage through OpenMetadata. - Infrastructure & DevOps: AWS (S3, EKS, EMR, MSK), Kubernetes, Terraform/IaC, GitLab/GitHub Actions, observability via Grafana/CloudWatch, and production runbook discipline. - BI & Analytics: Metabase, Superset, Tableau, and PowerBI for business dashboards; strong ability to model curated marts, fact/dimension tables, SCDs, and OBT patterns for stakeholder-facing analytics.
- Streaming Platform: MSK / Kafka for event-driven ingestion, CDC propagation, replay, backfills, and operational monitoring through Kafka UI. - Query & Serving Layer: Trino over Alchemy/Iceberg for lakehouse analytics, ClickHouse for high-throughput operational and real-time dashboards, and BigQuery exposure where applicable. - Workflow Orchestration: Airflow for scheduled data pipelines, DQ/reconciliation DAGs, backfills, and SLA-driven jobs; Temporal for durable workflow execution in ingestion services.
- Infrastructure & Ops: Managing data workloads on AWS (EMR, EKS, MSK, S3) and automating everything via Gitlab/Github Actions. - Query & BI: Tuning Trino or Clickhouse to power real-time dashboards in Metabase, Superset, and PowerBI. Our Tech Stack-
Our Tech Stack- - Ingestion & CDC: OLake and PeerDB for near real-time sync from production systems into Alchemy; Debezium/Kafka for CDC-heavy use cases, with support for Kafka and S3-based sources. - Lakehouse / Storage: Alchemy on Apache Iceberg with S3 as the data lake storage layer and AWS Glue Catalog for metadata; exposure to Delta/Hudi is a plus.
- Processing & Compute: PySpark on EMR/EKS for batch and streaming workloads; Flink and Spark Structured Streaming fundamentals for low-latency pipelines. - Streaming Platform: MSK / Kafka for event-driven ingestion, CDC propagation, replay, backfills, and operational monitoring through Kafka UI. - Query & Serving Layer: Trino over Alchemy/Iceberg for lakehouse analytics, ClickHouse for high-throughput operational and real-time dashboards, and BigQuery exposure where applicable.
- Infrastructure & DevOps: AWS (S3, EKS, EMR, MSK), Kubernetes, Terraform/IaC, GitLab/GitHub Actions, observability via Grafana/CloudWatch, and production runbook discipline. - BI & Analytics: Metabase, Superset, Tableau, and PowerBI for business dashboards; strong ability to model curated marts, fact/dimension tables, SCDs, and OBT patterns for stakeholder-facing analytics. Perks That Go Beyond the Paycheck
- Lakehouse / Storage: Alchemy on Apache Iceberg with S3 as the data lake storage layer and AWS Glue Catalog for metadata; exposure to Delta/Hudi is a plus. - Processing & Compute: PySpark on EMR/EKS for batch and streaming workloads; Flink and Spark Structured Streaming fundamentals for low-latency pipelines. - Streaming Platform: MSK / Kafka for event-driven ingestion, CDC propagation, replay, backfills, and operational monitoring through Kafka UI.
- Infrastructure: Hands-on experience with AWS (S3, EKS, MSK) and Infrastructure-as-Code. - Orchestration: Experience with Airflow or Temporal for complex workflow management. - AI-Native: Proficiency in using AI tools (Claude, Codex, Copilot) to write, test, and document code efficiently.
- Query & Serving Layer: Trino over Alchemy/Iceberg for lakehouse analytics, ClickHouse for high-throughput operational and real-time dashboards, and BigQuery exposure where applicable. - Workflow Orchestration: Airflow for scheduled data pipelines, DQ/reconciliation DAGs, backfills, and SLA-driven jobs; Temporal for durable workflow execution in ingestion services. - Data Quality & Governance: DQ checks, freshness/SLA monitoring, source-to-lake reconciliation, deduplication, schema evolution handling, and cataloging/lineage through OpenMetadata.
- Lakehouse Architecture: Designing and optimizing table formats (Iceberg, Delta, Hudi) for both performance and storage efficiency. - Unified Compute: Developing robust ETL/ELT frameworks in PySpark and Flink (handling both batch and streaming workloads). - Infrastructure & Ops: Managing data workloads on AWS (EMR, EKS, MSK, S3) and automating everything via Gitlab/Github Actions.
Posting record
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- Live
- First seen by us
- Aug 21, 2026 · 71 days ago
- Recorded sightings
- 34
- Last seen by us
- Oct 31, 2026
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Job description
About Fam (previously FamPay)
Fam is India’s first payments app for everyone above 11. FamApp helps make online and offline payments through UPI and FamCard. We are on a mission to raise a new, financially aware generation, and drive 250 million+ young users in India to kickstart their financial journey super early in their life.
We’re reimagining how the next generation experiences fintech—going beyond payments to build a lifestyle brand that blends money, identity, and everyday experiences into one seamless, intuitive journey.
Founded in 2019 by IIT Roorkee alumni, Fam is backed by some of the most respected investors around the world like Elevation Capital, Y-Combinator, Peak XV (Sequoia Capital) India, Venture Highway, Global Founder’s Capital and the likes of Kunal Shah, Amrish Rao as angel investors.
About the role:-
We are looking for a Data Engineer II (SDE-2) to join our data team. The ideal candidate will be a play a key role to develop of high performant and scalable Data Lake-house, moving us toward a world of sub-minute data latency and unified batch/streaming compute. This is an engineering-heavy role where you will manage complex CDC flows, optimize distributed query engines and leverage AI to accelerate our development lifecycle.
Must Have Requirements-
- Experience: 3–5 years in Data Engineering, specifically with distributed systems and cloud-native architectures.
- Coding: Expert-level Python/PySpark and SQL. Familiarity with Go/Java/Scala is a plus
- Infrastructure: Hands-on experience with AWS (S3, EKS, MSK) and Infrastructure-as-Code.
- Orchestration: Experience with Airflow or Temporal for complex workflow management.
- AI-Native: Proficiency in using AI tools (Claude, Codex, Copilot) to write, test, and document code efficiently.
- Systems Thinking: Ability to explain the trade-offs between different storage formats and processing frameworks.
- Tech Execution : Drive key tech initiatives by preparing TRD and actively involve in design reviews.
- Domain Modelling - Should be hands on in designing Domain models for OLAP like Fact, Dimension, Cumulative , types of SCD’s and OBT pattern tables.
- Self Starter - Lead the team technically and bring in new ideas to contribute to the growth of the charter.
- Stakeholder Interaction - Interact with the Product & Key Stakeholders & help them by adding value to the business workflow with data & analytics.
Good to have -
- Real-time CDC: Ownership of high-throughput ingestion from RDBMS to Lakehouse using Debezium, PeerDB.
- Lakehouse Architecture: Designing and optimizing table formats (Iceberg, Delta, Hudi) for both performance and storage efficiency.
- Unified Compute: Developing robust ETL/ELT frameworks in PySpark and Flink (handling both batch and streaming workloads).
- Infrastructure & Ops: Managing data workloads on AWS (EMR, EKS, MSK, S3) and automating everything via Gitlab/Github Actions.
- Query & BI: Tuning Trino or Clickhouse to power real-time dashboards in Metabase, Superset, and PowerBI.
Our Tech Stack-
- Ingestion & CDC: OLake and PeerDB for near real-time sync from production systems into Alchemy; Debezium/Kafka for CDC-heavy use cases, with support for Kafka and S3-based sources.
- Lakehouse / Storage: Alchemy on Apache Iceberg with S3 as the data lake storage layer and AWS Glue Catalog for metadata; exposure to Delta/Hudi is a plus.
- Processing & Compute: PySpark on EMR/EKS for batch and streaming workloads; Flink and Spark Structured Streaming fundamentals for low-latency pipelines.
- Streaming Platform: MSK / Kafka for event-driven ingestion, CDC propagation, replay, backfills, and operational monitoring through Kafka UI.
- Query & Serving Layer: Trino over Alchemy/Iceberg for lakehouse analytics, ClickHouse for high-throughput operational and real-time dashboards, and BigQuery exposure where applicable.
- Workflow Orchestration: Airflow for scheduled data pipelines, DQ/reconciliation DAGs, backfills, and SLA-driven jobs; Temporal for durable workflow execution in ingestion services.
- Data Quality & Governance: DQ checks, freshness/SLA monitoring, source-to-lake reconciliation, deduplication, schema evolution handling, and cataloging/lineage through OpenMetadata.
- Infrastructure & DevOps: AWS (S3, EKS, EMR, MSK), Kubernetes, Terraform/IaC, GitLab/GitHub Actions, observability via Grafana/CloudWatch, and production runbook discipline.
- BI & Analytics: Metabase, Superset, Tableau, and PowerBI for business dashboards; strong ability to model curated marts, fact/dimension tables, SCDs, and OBT patterns for stakeholder-facing analytics.
Perks That Go Beyond the Paycheck
• Relocation assistance to make your move seamless.
• Free office meals (lunch & dinner).
• Generous leave policy, including birthday leave, period leave, paternity and maternity support, and more.
• Salary advance and loan policies for any financial help.
• Quarterly rewards and recognition programs, and a referral program with great incentives.
• Access the latest gadgets and tools.
• Comprehensive health insurance for you and your family, mental health support.
• Tax benefits with options like food coupons, phone allowances, car/device leasing.
• Retirement perks like PF contribution, leave encashment and gratuity.
Here’s all the tea on FamApp ☕️
FamApp focuses on financial inclusion of the next generation by providing UPI & card payments to everyone above 11 years old. Our flagship Spending Account, FamX, seamlessly integrates UPI and card payments, enabling users to manage, save, and learn about their finances effortlessly.
Revolutionizing Payments and FinTech
FamApp has enabled 10 million+ users to make UPI and card payments across India, removing the inconvenience of carrying cash everywhere. Users get to customise their FamX card with doodles, which lets them add a personal touch to their payments.
Trusted by leading investors
We’re proud to be supported by renowned investors like Elevation Capital, Y-Combinator, Peak XV (formerly Sequoia Capital India), Venture Highway, Global Founder’s Capital, and esteemed angels Kunal Shah and Amrish Rao.
Join Our Dynamic Team
At Fam, our people-first approach is reflected in our generous leave policies, flexible work schedules, comprehensive health benefits, and free mental health sessions. We don’t mean to brag, but we promise you’ll be surrounded by some of the most fun, talented and passionate people in the startup space.
Want to see what makes life at Fam so awesome? Check out our shenanigans at @lifeatfam 😛
PS - We’re looking for someone with an AI-native mindset, who actively leverages AI in their day-to-day work, as we encourage and embed it across every level at Fam.
Employment type
Fulltime- Office
Source details
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Bengaluru
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