Senior Data Engineer

Responsibilities

What You'll Own

1. Lakehouse Pipelines & Ingestion (35%)

Design and own batch ETL/ELT and CDC pipelines that bring sensor and operational data into the lakehouse, orchestrated in Dagster

Build for reliability: idempotent, incremental, backfill-safe pipelines with sane retry and failure handling, that still produce correct output when a worker is killed mid-run or a message is delivered twice

Onboard new client data sources: schema and tag mapping, time-series normalization, resampling, gap handling at scale

2. Modeling & Serving (25%)

Model raw data into well-structured, documented tables that downstream ML and analytics can trust

Build and maintain the datasets behind ML feature pipelines and the lakehouse layer powering self-serve analytics

Write performant Spark/PySpark and SQL; optimize partitioning, storage formats, and query cost

3. Data Quality & Reliability (10%)

Own data quality: validation, freshness/SLA monitoring, and observability so bad data is caught before it reaches consumers

Make the data layer debuggable: lineage, tests, and alerting that tell you what broke and where

Reason about failure modes across the whole path (queue, worker, orchestrator, database, object store) and design so that a partial failure leaves the system in a state you can recover from

4. Relational & Operational Data (30%)

Contribute to the schema, indexing, and query performance of the relational database the product runs on

Design tables and constraints so that correctness is enforced at the database layer, and diagnose slow queries from their plans

Own retention and the boundary between the operational database and the lakehouse: what stays, what moves, and how it gets there

What Success Looks Like

First 30 days: Productive in the codebase and orchestration layer. First pipeline change merged.

First 90 days: Independently shipping and owning pipelines. Onboarded at least one new data source end-to-end.

First 6 months: Owning a lakehouse data domain, its ingestion, models, and quality, that ML and analytics teams rely on you to drive.

Requirement

Must-Have

1. 4+ years building production data pipelines: real systems with real consumers, not just one-off scripts
2. Strong data engineering fundamentals: data modeling, batch vs. streaming, idempotency, incremental processing, partitioning.
3. Expert SQL and strong Python: query optimization, window functions, clean production-quality code
4. Relational database depth: you've designed schemas for a production PostgreSQL (or equivalent) system and understand normalization and when to break it, indexing strategies, transactions and isolation levels, locking, and how to read a query plan and fix the query
5. Distributed systems fundamentals: at-least-once delivery and idempotent consumers, partitioning and its effect on ordering, consistency and durability trade-offs, retries, timeouts, and backpressure. You can explain what happens to in-flight work when a worker or a database node dies
6. Hands-on with a distributed processing engine (Spark/PySpark or equivalent) on non-trivial data volumes
7. Experience with an orchestrator (Dagster, Airflow, Prefect, or equivalent) and a cloud platform (AWS/Azure)
8. Data-quality mindset: you build validation and monitoring into pipelines, not after something breaks

Strong Signals (Nice-to-Have)

1. Time-series or high-frequency sensor data at scale
2. TimescaleDB or another time-series database (hypertables, continuous aggregates, compression, retention policies)
3. Warehouse/lakehouse modeling (Delta/Iceberg/Snowflake/Redshift or equivalent) and file-format/partition tuning (Parquet)
4. CDC / database-replication pipelines
5. Message brokers or task queues in production (Kafka, RabbitMQ, or equivalent)
6. Building data for ML: feature pipelines, training datasets, serving consistency
7. dbt or similar transformation/modeling frameworks
8. Docker, Terraform/IaC, CI/CD for data
9. IoT, energy, or industrial sector experience. Not required, but it compresses your ramp

Job Details

Gurugram - Hybrid (2–3 days on-site)

Interview Process

• Technical Round (Python & SQL)
• System Design Round
• Culture Fit Round