Altimetrik Data Engineer Interview Questions (2026)
Prepare for your technical rounds at Altimetrik with 40+ reported questions across SQL, Apache Spark, Python, and System Design. Compare typical candidate answers against FAANG 9/10 benchmarks, and test your own solutions with instant AI feedback.
Interview Topic Weightage at Altimetrik
Based on verified interview loopsUnlocked FAANG-Calibrated Answer Rubric (Top 3 Tested Questions)
- •Structured delivery following Problem Formulation -> Execution Plan -> Failure Recovery
- •Concrete parameters: partition keys, join strategies, memory limits, and I/O costs
- •Explicit trade-off evaluation (e.g. storage cost vs query latency, exactly-once overhead)
Complete Altimetrik Question Vault (37 Questions)
Showing 15 of 37What are Airflow Operators? Give examples.
Airflow Operators define a single unit of work in a DAG—each operator performs one atomic, idempotent task. **Why they matter**: They encapsulate work so DAGs remain declarative and schedulable; the scheduler doesn't need to understand task logic. **Examples**: BashOperator, PythonOperator, SqlOperator, HTTPOperator, DockerOperator, KubernetesPodOperator, Sensor. **Scalability**: Heavy logic should live in external scripts or services; operators should only orchestrate....
How would you read data from a web API? What steps would you follow after reading the data?
**Why this matters**: APIs are external dependencies—unreliable, rate-limited, schema-evolving. Production ingestion must be resilient, auditable, and idempotent. **Steps**: (1) **Contract & auth**: Define schema (Pydantic, JSON Schema); store credentials in secrets manager (Vault, AWS Secrets). OAuth tokens need refresh logic. (2) **Pagination & rate limiting**: Offset/cursor-based; loop until empty. Respect Retry-After; implement exponential backoff....
Architecturally, how would you justify or challenge Hadoop vs. a cloud-native data lake (S3 + EMR/Databricks) for a greenfield enterprise data platform? Discuss scalability ceilings, cost model trade-offs, and operational complexity.
Hadoop's architecture centers on HDFS (storage) + YARN (resource scheduling) + MapReduce (compute model). Components: Namenode (single point for metadata—scalability ceiling for namespace); Datanodes (block storage, 128MB default blocks, 3x replication); ResourceManager/NodeManagers for CPU/memory allocation. Why it matters architecturally: HDFS scales linearly with nodes but metadata scale is bounded by Namenode heap; YARN allows multi-tenant workloads but adds operational overhead....
How would you read data from a web API using PySpark?
PySpark has no native API source; the pattern is driver fetch or executor fetch. **Approaches**: (1) **Driver + parallelize**: data = requests.get(url).json(); df = spark.createDataFrame(data). Scales to API response size (typically MBs); driver is bottleneck. (2) **mapPartitions on executor**: Pass partition of IDs to each task; each calls API. Scales to many IDs but risks rate limiting and API abuse. (3) **Orchestrator + landing**: Airflow/Prefect fetches API → lands to S3/GCS → Spark reads....
What is broadcasting in Spark, and why is it used? Can you give an example of its use?
Broadcasting sends a small table replica to every executor so joins execute locally without shuffle. **Why it matters**: A shuffle join moves both sides across the network; broadcast join moves only the small side once from driver to executors. **Scalability trade-off**: The small table must fit in executor memory; exceeding this causes OOM. As cluster size grows, more copies exist (N executors × table size), so large broadcasts waste memory....
What is the difference between Managed and External Tables in Databricks?
**Managed tables**: Databricks/Spark owns both metadata and data; `DROP TABLE` deletes metadata and underlying data. **External tables**: Metadata is in the catalog; data lives in an external location (S3, ADLS, GCS); `DROP TABLE` removes only metadata; data persists. **Why it matters architecturally**: Managed tables enforce a single lifecycle for schema and data. External tables decouple storage from compute, enabling multi-engine access (Snowflake, Athena, Redshift) and shared data lakes....
What is the difference between map and flatMap in Spark, and when would you use each?
**map**: 1 input element → 1 output element; output collection has same cardinality. **flatMap**: 1 input element → 0 or more output elements; results are flattened into a single collection. **Why it matters**: flatMap controls cardinality explosion (e.g., tokenizing a line into N words) and avoids nested structures that would require a separate explode step....
What work is done by the executor memory in Spark?
Executor memory holds: **(1)** Cached RDD/DataFrame partitions (storage fraction), **(2)** Shuffle output written by map tasks for reduce tasks, **(3)** Working memory for task execution (joins, aggregations, sorting), **(4)** Off-heap (e.g., for native operations, Tungsten). **Why it matters**: Execution memory competes with storage; undersized executors cause spills and OOM....
When and how do you use Broadcast Join?
**When**: One join side fits in memory across executors (typically < ~100MB, configurable via `spark.sql.autoBroadcastJoinThreshold`). **How**: Use `broadcast(df)` hint or rely on Spark auto-broadcast. Driver sends the small table to all executors; join runs locally without shuffle of the large table. **Why**: Shuffle of a large fact table is expensive (network, disk, serialization); broadcasting the dimension avoids it....
Why is SparkSession used in Spark 2.0 and later versions?
**SparkSession** (2.0+) is the unified entry point for DataFrames, Datasets, SQL, and Structured Streaming. It subsumes SparkContext, SQLContext, HiveContext, and StreamingContext. **Why it matters**: Single API for DataFrame/SQL/Streaming reduces boilerplate and simplifies configuration. One session manages config, catalog, and context. **Architectural benefit**: Consistent behavior across Scala, Python, R; easier migration from RDD to DataFrame; Hive support via `.enableHiveSupport()`....
Write a Python script to find the count of each word in a text file using Spark.
To count word occurrences in a text file using Spark, the standard approach leverages the PySpark DataFrame API to read the file, tokenize lines into words, and then aggregate these words. This method…
Write the PySpark code to find the second highest salary in each department.
To find the second highest salary in each department using PySpark, the most robust and idiomatic approach leverages window functions. Specifically, you partition the data by department, order it by…
Explain the difference between a list and a tuple in Python.
Python's list and tuple are both ordered collections, but their fundamental difference lies in their mutability : lists are mutable, meaning their elements can be changed after creation, while tuples…
How do you handle exceptions in Python? Provide an example.
Python handles exceptions using the try , except , else , and finally blocks. This mechanism is crucial for building robust data pipelines, preventing crashes, and ensuring critical operations like…
What is the difference between shallow copy and deep copy in Python?
A shallow copy creates a new top level object but populates it with references to the original object's nested elements. This means changes to mutable nested objects in the copy will also affect the…
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Frequently Asked Questions About Altimetrik Data Engineering Interviews
What topics are asked most frequently in Altimetrik data engineering interviews?▼
Technical interviews at Altimetrik heavily emphasize Spark/Big Data and Python/Coding, accounting for the majority of technical questions in our repository of 40+ verified interview questions. Candidates should expect hands-on rounds testing query optimization, data skew mitigation, and end-to-end pipeline architecture.
How deep do Altimetrik interviewers go on Apache Spark, SQL, and system design?▼
Interviewers at Altimetrik probe far beyond syntax. They expect candidates to discuss Spark DAG execution plans, shuffle partitions, join selection strategies (broadcast hash vs shuffle sort-merge), and handling un-even data distributions. In system design, emphasis is placed on fault tolerance, backfilling, and SLA preservation under high data volume.
What is the typical technical interview loop format for Data Engineers at Altimetrik?▼
The Altimetrik interview process typically consists of: 1) Initial Recruiter Screen (30 mins), 2) Technical Phone Screen focusing on live SQL and data manipulation (45-60 mins), followed by a 4-5 round Virtual Onsite covering SQL & Coding, Distributed Pipeline Architecture, Data Modeling, and a Behavioral/Bar-Raiser session.
How can I practice Altimetrik data engineer interview questions with AI feedback?▼
You can practice directly on DataEngPrep.tech. Select any question from the Altimetrik library, submit your answer, and receive an instant FAANG-calibrated evaluation scored across accuracy, technical depth, communication, and completeness, complete with an interview-ready model answer rewrite.